Prompt:
Anthony Rizzo
Anthony Vincent Rizzo (Parkland, Florida, 8 de agosto de 1989) es un beisbolista profesional estadounidense, que juega en la primera base de los New York Yankees, de las Grandes Ligas de Béisbol (MLB). Anteriormente fue parte de los San Diego Padres y de los Chicago Cubs. Fue tres veces convocado para el Juego de Estrellas. Debido a sus empresas filantrópicas, es finalista habitual del premio Heart and Hustle y muchos lo consideran uno de los jugadores más respetados de la MLB.

Rizzo fue seleccionado por los Boston Red Sox, en la sexta ronda del draft de la MLB de 2007, y se convirtió en uno de los principales prospectos de ligas menores en la organización. Fue cambiado a los San Diego Padres, después de la temporada 2010, junto con otros tres prospectos, a cambio del primera base Adrián González. Hizo su debut en la MLB, en el 2011, con San Diego. Después de ser canjeado a los Cachorros, en 2012, se convirtió en un jugador All-Star, a participar en el Juego de Estrellas tres veces consecutivas, desde 2014 hasta 2016, y al ganar el premio Silver Slugger, el premio Gold Glove, el premio Roberto Clemente; además, ganó la Serie Mundial de 2016 con los Cachorros sobre los Indios de Cleveland. Los Cachorros lo cambiaron a los Yankees durante la temporada 2021.

Carrera profesional

Boston Red Sox
Rizzo fue seleccionado por Boston en la sexta ronda del draft de 2007, procedente de Marjory Stoneman Douglas High School, en Parkland, Florida.  Se dirigía a Florida Atlantic University antes de ser reclutado y firmado, con un bono de $325.000.  Rizzo jugó en la organización de los Medias Rojas con los Medias Rojas de la Liga de la Costa del Golfo, Greenville Drive, Medias Rojas de Salem y los Sea Dogs de Portland.  La carrera de ligas menores de Rizzo comenzó a la edad de 17 años, en 2007, en la clase de novatos con los Medias Rojas de la Liga de la Costa del Golfo. En solo 21 turnos al bate, alcanzó cifras ofensivas de.286/.375/.429 con 1 jonrón y 3 carreras impulsadas.  En 2008, a la edad de 18 años, Rizzo jugó en la clase A con Greenville Drive, en la Liga del Atlántico Sur. En 83 turnos, bateó.373/.402/.446 con 0 jonrones y 11 impulsadas.  Rizzo conectó 12 cuadrangulares en 2009.  En 2010, bateó un promedio combinado de.260 con un porcentaje de embasado (OBP) de.334 y un porcentaje de slugging (SLG) de.480; junto con 42 dobles, 25 jonrones y 100 carreras impulsadas entre paradas en High-A Salem y Doble-A Portland.  Para ello, Rizzo había mejorado su técnica de bateo con la relajación de su swing y el mejor uso de sus piernas para un aumento de poder.
miniaturadeimagen| Rizzo bateando para los Padres de San Diego en 2011
El 6 de diciembre de 2010, Rizzo fue cambiado, junto con Casey Kelly, Reymond Fuentes y Eric Patterson, a los Padres por el primera base y tres veces All-Star, Adrián González.   Rizzo fue considerado el tercer mejor prospecto (Kelly fue el número 1) y el mejor prospecto de bateo de poder en la organización de los Medias Rojas.   Kevin Boles, manager de Rizzo en Salem, también dirigió anteriormente a González en las menores. Boles dijo: "Rizzo me recuerda mucho a Adrián González... Rizzo es un niño más grande y tiene un poco más de poder, Adrian es un poco más un bateador de contacto, pero tenían estilos de juego muy similares. Pensamos muy bien en Anthony Rizzo. Va a ser un gran jugador".  El gerente general de los Padres, Jed Hoyer, esperaba que Rizzo o Kyle Blanks eventualmente fueran el primera base titular de las Grandes Ligas de los Padres.

Los Padres invitaron a Rizzo a su campamento de Grandes Ligas para el entrenamiento de primavera de 2011.  Comenzó ese año en Triple-A, con los Padres de Tucson.  En sus primeros 15 juegos, Rizzo bateó.452 con 6 jonrones y 24 carreras impulsadas.  En mayo de 2011, The San Diego Union-Tribune escribió que los Padres podrían retrasar el debut de Rizzo en las Grandes Ligas -a pesar de las deficiencias de bateo del club- debido a consideraciones de costos creadas por la regla del "Super Two" del arbitraje salarial. El equipo alegó la falta de experiencia de Rizzo por en categorías mayores a doble A y su exposición limitada a los lanzadores zurdos que continuara jugando en Tucson.

Rizzo fue llamado a las mayores luego de batear.365 con un OPS de 1.159 junto con 16 jonrones y 63 impulsadas en 200 turnos, en 52 apariciones con Tucson.   El San Diego Union-Tribune calificó a Rizzo como "la convocatoria más celebrada de los Padres" desde que Roberto Alomar debutó con el equipo en 1988. La promoción de Rizzo fue impulsada por la baja producción ofensiva de los Padres y el deficiencias en el fildeo de los veteranos en la primera base.  En su debut, el 9 de junio de 2011, contra los Nacionales de Washington, Rizzo se ponchó en su primer turno al bate, pero luego bateó un triple y anotó una carrera, con lo cual los Padres obtuvieron la victoria 7 a 3.  Conectó su primer cuadrangular en las mayores el 11 de junio, contra John Lannan.  Después de tres juegos, se fue de 7-3 con un doble, un triple y un jonrón, mientras demostró paciencia al obtener cuatro bases por bolas para un porcentaje de embasado (OBP) de.667.  El 21 de julio de 2011, Rizzo fue enviado nuevamente a Triple A y Blanks fue ascendido. Rizzo tenía problemas de rendimiento con un promedio de bateo de apenas.143 y 1 jonrón, ponchándose 36 veces en 98 turnos al bate. Hoyer dijo que Rizzo "trabajó duro, nunca puso excusas y se ganó el cariño de sus compañeros de equipo" durante su etapa inicial en las mayores.  Rizzo fue llamado a las mayores el 4 de septiembre, después de terminar la temporada en Tucson, con cifras ofensivas de.331 con 26 vuelacercas y 101 empujadas en 93 juegos.  Terminó su primera temporada en San Diego bateando solo.141 con 46 ponches en 128 turnos. Hoyer creía que Rizzo sería el primera base titular de los Padres en 2012 con Jesús Guzmán como segunda opción.  Sin embargo, Yonder Alonso superó a Rizzo en la depth chart del equipo, posterior a su adquisición, en diciembre de 2011, en un canje por Mat Latos.

Chicago Cubs
miniaturadeimagen| Rizzo (derecha) jugando en primera base para los Cachorros de Chicago en 2012
El 6 de enero de 2012, los Padres cambiaron a Rizzo y al lanzador abridor derecho Zach Cates a los Chicago Cubs, a cambio del lanzador abridor derecho Andrew Cashner y el jardinero Kyung-Min Na. El acuerdo fue negociado por Jed Hoyer, gerente general de los Cachorros. Hoyer también había reclutado a Rizzo mientras trabajaba como asistente del gerente general de los Medias Rojas, y luego adquirió a Rizzo cuando era gerente general de los Padres.  Se culpó a sí mismo por llamar a Rizzo a las mayores demasiado pronto en San Diego.

2012
Rizzo comenzó la temporada 2012 con los Triple-A Iowa Cubs. Volvió a sobresalir en las ligas menores con un promedio de bateo de.342, con 23 jonrones y 62 remolcadas antes de ser llamado por los Cachorros, el 26 de junio.  Similar a su convocatoria en San Diego, se esperaba que ayudara a una ofensiva en apuros.

Se convirtió en el primer jugador en la historia de los Cachorros en lograr tres carreras impulsadas ganadoras en sus primeros cinco juegos con el equipo. Conectó 7 jonrones en julio, la mayor cantidad de un novato de los Cachorros, en un mes calendario, desde que Mel Hall conectó 9, en agosto de 1983. Ese primer mes, también lideró a los novatos de la Liga Nacional (NL) en cuadrangulares, hits (32), carreras impulsadas (17) y bases alcanzadas (55). Ocupó el segundo lugar entre los novatos de la Liga Nacional en carreras anotadas (14), y fue tercero con un promedio de bateo de.330, porcentaje de embasado de.375 y porcentaje de slugging de.567. Fue nombrado Novato del mes de julio de la liga.

2013
El 12 de mayo de 2013, Rizzo acordó un contrato de 7 años y 41 millones de dólares. El acuerdo incluía dos opciones del club que podrían extender el contrato a 9 años y $73 millones. Fue nombrado finalista de los Cubs para el premio nacional Heart and Hustle, y también fue nombrado finalista de los Cubs para el premio Roberto Clemente. Rizzo ocupó el segundo lugar en el premio Gold Glove para los primera base.  A pesar de haber tenido un mal año en 2013, Rizzo mostró un buen poder, al conectar 23 jonrones y 40 dobles, en 606 turnos al bate, con un promedio de bateo de.233.

2014
Rizzo tuvo su quinto juego de varios jonrones el 30 de mayo,  y el 6 de junio dio su segundo jonrón para dejar en el campo al rival (walk-off).  Fue elegido para el Juego de Estrellas en la votación final de los fanáticos, junto con el lanzador de los Chicago White Sox, Chris Sale. A fines de julio, ganó por primera vez el Jugador de la Semana.  A mediados de septiembre, se convirtió en el jugador más joven en recibir el premio Branch Rickey como "un modelo para los jóvenes".  Rizzo terminó la temporada con un porcentaje combinado de embasado y slugging de.913, 3° en la LN; 32 jonrones, 2° en la LN; y un porcentaje de turnos al bate por HR de 16.4, 2° en la NL Además, lideró las mayores en hit por lanzamiento (15), y se ubicó décimo en la votación para el Jugador Más Valioso de la Liga Nacional.

2015
Rizzo fue elegido en la boleta de los jugadores para el Equipo de Estrellas por segundo año consecutivo. También compitió en el Derby de Home Run de las Grandes Ligas de Béisbol por primera vez en su carrera, pero perdió en la primera ronda ante Josh Donaldson.  Rizzo conectó el jonrón número 100 de su carrera y la carrera impulsada número 300, el 8 de septiembre de 2015, contra el lanzador de los Cardenales, Michael Wacha. Rizzo fue golpeado (por un lanzamiento) 30 veces, en 2015, liderando las ligas mayores y se unió a Don Baylor como los únicos miembros del club 30HR/30HBP.   Rizzo terminó la temporada regular con un promedio de bateo de.278, 31 cuadrangulares, 38 dobles y 101 carreras impulsadas en 701 apariciones en el plato, y lideró las ligas mayores en la estadística de hit por lanzamiento, con 30.  Ocupó el cuarto lugar en la votación de MVP de la Liga Nacional.  Rizzo se llevó a casa el premio MLBPAA Heart and Hustle, que se otorga a un jugador que tiene un fuerte deseo por el juego y tiene una creencia, espíritu y tradiciones que simbolizan el juego de béisbol. Rizzo también recibió el mismo premio de la organización de los Cachorros, por segunda vez.

2016
derecha|miniaturadeimagen| Rizzo (derecha) con David Ortiz durante el Home Run Derby de 2016
Rizzo fue titular en primera base en el Juego de Estrellas de 2016 y recibió la mayor cantidad de votos de los fanáticos en la Liga Nacional.  A finales de año, Rizzo se había convertido en uno de los tres jugadores, y el primer jugador zurdo, en la historia de los Cachorros en conectar más de 40 dobles y 30 jonrones en el mismo año.  Participó en 155 juegos con 583 turnos al bate y anotó 94 carreras. Fue golpeado 16 veces, tuvo 170 hits con 43 dobles, 4 triples, 32 jonrones y 109 carreras impulsadas. Terminó el año con un promedio de bateo de.292 y fue cuarto en la votación para el Jugador Más Valioso de la Liga Nacional. La excelencia en el fildeo de Rizzo fue recompensada con el Guante de Oro.  Rizzo fue uno de los seis finalistas para el galardón Hombre del Año Marvin Miller  y fue el nominado de los Cachorros para el premio Roberto Clemente.  Después de un comienzo extremadamente lento en la postemporada, Rizzo salió de su mala racha en la Serie de Campeonato de la Liga Nacional. Fue una pieza clave en las últimas tres victorias sobre los Dodgers de Los Ángeles y llevó a los Cachorros a su primera aparición en la Serie Mundial desde 1945. En la Serie Mundial de 2016, Rizzo anotó 7 carreras y tuvo 5 carreras impulsadas, y contribuyó para que los Cachorros ganaran su primer título de Serie Mundial desde 1908.  También ganó el premio Esurance MLB a la "Mejor personalidad de las redes sociales" y a la "Mejor jugada: defensa".  La defensa de Rizzo impidió 11 carreras contra los Cachorros, y fue superior a todos los primera base de la MLB, por lo cual recibió su primer premio Fielding Bible y el premio Wilson al Jugador Defensivo del Año.  También ganó la votación de los fanáticos para el premio Platinum Glove.  Rizzo se llevó a casa dos premios más del año. Uno era el Silver Slugger. Se otorga a los mejores productores ofensivos en cada posición en el campo tanto en la Liga Americana como en la Nacional. Era la primera vez que Rizzo recibía el premio. El último fue el premio MLBPAA Cubs Heart and Hustle.  Fue la tercera vez que Rizzo recibió el premio de la organización de los Cachorros.
miniaturadeimagen| Rizzo celebra el out final de la Serie Mundial 2016
miniaturadeimagen| Rizzo le presenta al presidente Obama una camiseta de los Cachorros, en una visita a la Casa Blanca, el 16 de enero de 2017
Con los Cachorros en una mala racha ofensiva ycon un promedio de victorias de.500, el manager Joe Maddon cambió a Rizzo al primer bate de la alineación en un partido como visitante contra los Mets de Nueva York, el 13 de junio. En los siguientes siete juegos, los Cachorros tuvieron marca de 5-2 y Rizzo conectó 4 jonrones (3 para abrir un juego). Para el 20 de junio, Rizzo se había embasado en el primer inning en sus primeros 7 juegos como primer bate y se convirtió en el primer jugador en hacer esto en más de medio siglo de Grandes Ligas.  Tuvo 12 hits en 28 turnos, con 10 carreras impulsadas y bateó.430 durante la racha. Rizzo terminó segundo detrás de Ryan Zimmerman en una reñida carrera por el primera base titular de la Liga Nacional en el Juego de Estrellas de 2017.  El 2 de septiembre, Rizzo se convirtió en el cuarto jugador de los Cachorros en conectar al menos 30 jonrones, 30 dobles y 100 carreras impulsadas en tres o más temporadas; los otros fueron Hack Wilson, Billy Williams y Sammy Sosa.

Durante la temporada, bateó.273/.392/.507 con 32 jonrones y 109 carreras impulsadas.  Lideró las mayores en hit por lanzamiento, con 24.

Rizzo tuvo una postemporada decepcionante. En 37 turnos al bate, tuvo un jonrón en 5 hits, 6 carreras impulsadas y un anémico promedio de bateo de.135.  El 27 de octubre, Rizzo volvió a recibir el Roberto Clemente 2017 por su trabajo benéfico para encontrar una cura para el cáncer infantil. Sobre ganar el premio, Rizzo dijo: "Esto es asombroso. El mayor premio que puedes ganar. Irá al frente y al centro frente a cualquier cosa que haya hecho".

2018
El 10 de abril de 2018, Rizzo fue colocado en la lista de lesionados por primera vez en su carrera en la MLB, debido a un problema en la espalda.  Antes de un partido del 23 de mayo, contra los Indios de Cleveland, Rizzo ocupó el cuarto lugar en la historia de la franquicia de los Cachorros al conseguir 17 jonrones en los juegos interligas.  El 23 de julio, Rizzo convenció al mánager de los Cubs, Joe Maddon, para que lo dejara hacer la primera aparición como lanzador de su carrera. Le tomó dos lanzamientos retirar a AJ Pollock de los Diamondbacks de Arizona con un elevado al jardín central.

Rizzo terminó su campaña de 2018 con un promedio al bate de.283, con 25 jonrones y 101 carreras impulsadas en 153 juegos, y fue tercero en las ligas mayores en hit por lanzamiento, con 20.  Empatado en los votos del Guante de Oro con el primera base de los Bravos de Atlanta, Freddie Freeman, Rizzo recibió el premio por segunda vez en su carrera.

2019
En 2019, Rizzo bateó.293/.405/.520 con 27 jonrones y 94 impulsadas. Lideró las ligas mayores en golpeados con 27.  También recibió el tercer Guante de Oro de su carrera.

2020
En la recortada temporada de 2020, Rizzo jugó en 58 juegos y terminó con una línea ofensiva de.222/.342/.414, 11 jonrones, 24 carreras impulsadas y 3 bases robadas. También recibió su cuarto Guante de Oro; el tercero consecutivo.  Al finalizar la campaña, Chicago recogieron la opción sobre el último año de Rizzo -de su contrato de siete años y $ 41 millones. que le pagaría a Rizzo $ 16.5 millones para la temporada 2021. Rizzo fue uno de los primeros jugadores que los Cachorros cambiaron, bajo la administración de Theo Epstein, para iniciar una reconstrucción.

2021
En 92 juegos para los Cachorros de Chicago, Rizzo bateó.248/.346/.446 con 14 cuadrangulares, 40 carreras impulsadas y 4 bases robadas. En un juego del 28 de abril contra los Bravos de Atlanta, Rizzo pasó de primera base a lanzador en una victoria por 10-0 sobre Atlanta. Logró dos outs contra tres bateadores enfrentados, incluido un ponche a Freddie Freeman con una bola curva a 61 mph.

New York Yankees

2021
El 29 de julio de 2021, Rizzo fue cambiado a los New York Yankees por Alexander Vizcaíno, Kevin Alcántara y dinero en efectivo.

Su primer partido con los Yankees fue el 30 de julio contra los Miami Marlins. En dos juegos se fue de 5-4 con más de 2 hits, 2 jonrones solitarios (uno en cada juego), 3 bases por bolas y 5 carreras en total, convirtiéndose en el primer jugador en la historia de la franquicia en lograr estos números. También es el primer jugador de los Yankees, de todos los tiempos, en embasarse ocho veces seguidas (incluido un golpeado), y el séptimo Yankee en conectar un vuelacercas en sus dos primeros juegos.

El 4 de agosto, Rizzo conectó un cuadrangular solitario contra los Baltimore Orioles, lo cual lo convirtió en el primer jugador en la historia del equipo en impulsar por lo menos una carrera en cada uno de sus primeros 6 juegos con los Yankees. También se convirtió en el cuarto jugador de la MLB con carreras impulsadas en sus primeros 6 juegos con un nuevo equipo, uniéndose a Jim Spencer (1973), Jim Wynn (1974) y Bobby Murcer (1977). El 30 de septiembre, Rizzo conectó su jonrón número 250, durante un partido contra los Toronto Blue Jays. Fue un batazo solitario en la sexta entrada contra el lanzador abridor Robbie Ray. Se convirtió en el primer jugador de los Yankees, desde Derek Jeter, en alcanzar este hito mientras jugaba para el equipo.

2022
El 17 de marzo de 2022, los Yankees firmaron a Rizzo con un contrato de $ 32 millones por dos años.

Carrera internacional
Como su familia es originaria de la ciudad siciliana de Ciminna,  Rizzo eligió jugar para Italia en el Clásico Mundial de Béisbol 2013 antes de la temporada 2013 de la MLB.

Vida personal
miniaturadeimagen| Rizzo acepta el premio Heart & Hustle en la cena 2015 MLBPAA Legends for Youth
Rizzo tiene un hermano mayor, John, que fue liniero del equipo de fútbol americano Florida Atlantic University.

A Rizzo se le diagnosticó linfoma de Hodgkin clásico, en estado limitado, en abril de 2008.  Pasó por quimioterapia durante seis meses.   Su abuela estaba luchando contra el cáncer de mama al mismo tiempo. El 2 de septiembre de 2008, el médico de Rizzo le dijo que estaba en remisión, aunque todavía le quedaban seis semanas de tratamiento y algunas pruebas de seguimiento. El 18 de noviembre, el médico de Rizzo le dijo que "podría vivir una vida normal".

Rizzo le propuso matrimonio a su novia, Emily Vakos, el 1° de junio de 2017.  Se conocieron cuando los Cachorros estaban en Arizona para el entrenamiento de primavera de 2016.  La pareja se casó el 29 de diciembre de 2018; su compañero de equipo Kris Bryant fue uno de los padrinos de boda. En el 2020, él y su esposa adoptaron un perro al que llamaron Kevin. Residen en Fort Lauderdale, Florida.  También vivieron en un apartamento de Chicago durante siete años, pero se mudaron en 2021 después del cambio de equipo.

Rizzo eligió "Tony" como su apodo para el Players Weekend durante la temporada 2017.  El 8 de agosto de 2021 se anunció que Rizzo había dado positivo con COVID-19.

Trabajo caritativo
En 2012, se fundó la Fundación de la Familia Anthony Rizzo. Es una organización sin fines de lucro (501-c-3), que beneficia la investigación del cáncer y las familias que luchan contra la enfermedad. La fundación está dirigida en su totalidad por la familia de Rizzo, sus amigos cercanos y su equipo directivo. Rizzo proporciona supervisión y liderazgo.  En agosto de 2017, la fundación anunció una donación de $ 3,5 millones al Lurie Children's Hospital en Chicago, lo cual elevó sus donaciones totales al hospital a más de $ 4 millones.

El 15 de febrero de 2018, Rizzo pronunció un emotivo discurso en la vigilia por las víctimas del tiroteo en la escuela en Parkland, Florida. Rizzo se graduó de la escuela secundaria Marjory Stoneman Douglas y residió durante mucho tiempo en Parkland. "Crecí en Stoneman Douglas [High School]", dijo un emocionado Rizzo.  Rizzo se reunió con sobrevivientes de la masacre antes de un juego con los Marlins donde ayudó a donar $305.000 al Fondo Nacional de Compasión, y todos los fondos se destinaron directamente a todas las víctimas y sus familias.

El 15 de mayo de 2015, la Fundación de la Familia Anthony Rizzo organizó su 3° concurso anual de cocina contra el cáncer y recaudó más de $270.000.  El 15 de noviembre de 2015, la Fundación de la Familia Anthony Rizzo organizó su 4° Marcha Anual contra el Cáncer y recaudó más de $200.000 para la investigación del cáncer pediátrico y brindar apoyo a los niños y sus familias.  El 2 de junio de 2016, la Fundación de la Familia Anthony Rizzo organizó su 4.° concurso anual de cocina contra el cáncer y recaudó más de $630.000.

La quinta marcha anual contra el cáncer se llevó a cabo el domingo 11 de diciembre de 2016 y The Anthony Rizzo Foundation recaudó más de $500.000. El comisionado del condado de Broward, Michael Udine, proclamó el domingo como el Día de Anthony Rizzo. La antigua escuela secundaria de Rizzo retiró oficialmente su camiseta, la número 7.  Rizzo y su fundación organizaron su sexta marcha anual contra el cáncer el 3 de diciembre de 2017 y recaudaron $960.000 para las familias que luchan contra el cáncer. Las ganancias netas del evento beneficiarán al Joe DiMaggio Children's Hospital, al Sylvester Comprehensive Cancer Center de la Universidad de Miami y otorgarán subvenciones a las familias que luchan contra el cáncer.  La séptima Marcha Anual contra el Cáncer organizada por la Fundación de la Familia Anthony Rizzo recaudó $1.1 millones el 2 de diciembre de 2018. El dinero se destinó al Joe DiMaggio Children's Hospital, al Sylvester Comprehensive Cancer Center de la Universidad de Miami y a familias que luchan contra el cáncer.

El 27 de mayo de 2019, la Fundación de la Familia Anthony Rizzo organizó su séptima competencia anual de cocina para el cáncer y recaudó $1.8 millones para pacientes con cáncer y sus familias.  El 24 de noviembre de 2019, la Fundación de la Familia Anthony Rizzo organizó su octava Marcha Anual contra el Cáncer y recaudó más de $1.35 millones.  El 16 de enero de 2020, la Fundación de la Familia Anthony Rizzo recaudó cerca de $500.000 para ayudar a las familias que luchan contra el cáncer durante su sexto evento anual Laugh-Off for Cancer.  En febrero de 2020, Rizzo donó $ 150.000 a la escuela secundaria Marjory Stoneman Douglas para ayudar a pagar las luces de la escuela para sus campos de béisbol y softbol. Su escuela secundaria inauguró el nuevo campo de béisbol que se conocerá como Anthony Rizzo Field.  La Fundación de la Familia Anthony Rizzo organizó su novena marcha anual contra el cáncer, el 15 de noviembre de 2020 y recaudó más de $850.000.

Referencias

Categoría:Beisbolistas de San Diego Padres
Categoría:Beisbolistas de New York Yankees
Categoría:Beisbolistas de los Chicago Cubs
Categoría:Beisbolistas de Florida
Categoría:Personas vivas
Categoría:Nacidos en 1989

In what year did Rizzo make his MLB debut and with what team? He debuted in 2011 with the San Diego Padres.


Prompt:
Starting from bottom to top can it be predicted the outcome of the next number (as red or green) at some point using the patterns  (green defined as >2) and the red numbers(defined as <2) in this list of numbers:1
2.76
1.03
1.08
1.66
1.42
1.17
1.25
2.75
1.06
1.69
2.19
4.39
1.13
1.83
1.24
2.07
24.51
4.81
11.1
2.42
1.08
1.89
1.16
2.56
323.92
4.57
2.47
1.77
1.8
1.8
46.3
1.26
5.21
1.02
2.69
1.88
1.94
3.3
4.82
1.28
43.1
4.75
27.68
1
381.51
2.51
28.37
1.34
1.51
1.42
1.27
6.47
4.39
1.29
1.45
10.23
2.35
1.01
1.22
4.97
1.4
6.56
1.2
1.8
8.05
1.13
3.71
2.15
2.1
1.46
1.17
4.48
1.38
56.68
7.75
1.65
3.29
1.28
28.31
1.85
1.26
8.11
1.33
1.82
27.61
1.14
1.74
1.15
1.33
11.78
3
3.34
1.66
1.27
1.07
1.2
1.39
1.44
1.62
1.04
1.43
4.29
2.96
1.02
1.26
1.13
1.46
3.07
1.84
4.95
1
1.36
17.34
2.87
2.26
1.07
3.13
2.16
1.3
16.64
1.37
1.28
4.08
1.54
1.75
1.68
15.67
1.44
3.86
4.34
1.68
1.06
7.18
7.73
1.11
1.5
1
12.7
1.04
3.23
2.32
10.85
9.92
1.25
2.1
4.68
3.35
3.45
350.31
133.94
2.23
1.59
2.25
1.13
2.13
2.36
1.32
2.42
1.11
1.47
4.2
2.13
57.27
1.02
2.93
1.49
1.93
1.27
6.67
1.54
1.32
1.64
1.39
1
4.98
2.07
7.25
2.35
2
4.34
1.13
5.27
93.38
1.28
3.19
1.28
1
1.05
1.07
2.15
37.28
4.89
1.27
1.03
11.41
3.18
1.35
1.42
1.36
2.27
17.83
1.32
56.53
1.24
4.99
1.04
1.22
43.33
2.99
4.32
3.21
1.46
135.55
1.73
15.68
2.03
1.57
1.25
15.7
1.09
3.29
7.68
2.98
1.4
1.79
4.44
1.22
2.64
1.31
9.34
1.17
1.67
1.43
1.44
1.5
2.48
11.41
1.45
1.87
1.17
2.57
2.69
1.52
10.87
5.11
1.22
1
1.48
1.74
2.83
1.27
4.1
3.02
1.26
3.03
18.31
3.19
5.58
1.11
1.92
1.45
40.35
1.15
1.77
1.95
7.92
4.01
2.14
1.18
1.37
1.26
1.56
1.35
1.3
1.3
3.64
2.19
10.22
2.8
1.67
1.82
22.05


Prompt:
**101 conversation starters** for couples

**GARY CHAPMAN**
& RAMON PRESSON

Northfield Publishing
CHICAGO
© 2002, 2012 BY GARY CHAPMAN & RAMON PRESSON

Formerly titled _Love Talks for Couples_
All rights reserved. No part of this product may be reproduced in any form without permission in writing from the publisher, except in the case of brief quotations embodied in critical articles or reviews.
Two entries (#37,#54) first appeared in Ramon Presson, _Soul Care_
(Littleton, Colo.: Serendipity House, 2000).
Cover design: Smartt Guys design
Cover photo: Steve Cole/iStock
Interior design: Julia Ryan / www.DesignByJulia.com
Gary Chapman photo: David Smith

ISBN: 978-0-8024-0837-2

We hope you enjoy this book from Northfield Publishing. Our goal is to provide high-quality, thought-provoking books and products that connect truth to your real needs and challenges.
For more information on other books and products written and produced from a biblical perspective, go to www.moodypublishers.com or write to:

Northfield Publishing
820 N. LaSalle Boulevard
Chicago, IL 60610

1 3 5 7 9 10 8 6 4 2

_Printed in the United States of America_

# Tips for Using 101 Conversation Starters

Your spouse is a fascinating person, a treasure trove of meaningful, humorous, and profound experiences, thoughts, feelings, ideas, memories, hopes, dreams, beliefs, and convictions. These questions celebrate the depth and wonderful mystery of your mate. Questions invite disclosure, and disclosure launches discovery. Discovery enriches a marriage and builds intimacy. Use the following 101 questions to prompt meaningful, in-depth discussions and to affirm and encourage your spouse.

Here are some ways to use the questions:

• During dinner at home (if you don't have children)

• During a quiet moment in the evening

• At bedtime (if both of you are alert)

• During dinner on a date night

• While in the car during a long drive

While the easiest way to proceed through the questions is to use them in the order they are presented, another possibility is that your spouse and you take turns in selecting the questions. We recommend that you do only one or two questions at a time. These questions are like dessert—a small and satisfying portion creates the anticipation for more later. _101 Conversation Starters for Couples_ offers a process to enjoy, not a project to complete.

Have fun with these questions two or three times each week and watch intimacy grow in your marriage.

# Table of Contnets

question 1

question 2

question 3

question 4

question 5

question 6

question 7

question 8

question 9

question 10

question 11

question 12

question 13

question 14

question 15

question 16

question 17

question 18

question 19

question 20

question 21

question 22

question 23

question 24

question 25

question 26

question 27

question 28

question 29

question 30

question 31

question 32

question 33

question 34

question 35

question 36

question 37

question 38

question 39

question 40

question 41

question 42

question 43

question 44

question 45

question 46

question 47

question 48

question 49

question 50

question 51

question 52

question 53

question 54

question 55

question 56

question 57

question 58

question 59

question 60

question 61

question 62

question 63

question 64

question 65

question 66

question 67

question 68

question 69

question 70

question 71

question 72

question 73

question 74

question 75

question 76

question 77

question 78

question 79

question 80

question 81

question 82

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question 85

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question 95

question 96

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question 98

question 99

question 100

question 101

# [question
1](9780802483560_epub_toc_r1.htm#c01a)

What are two things that happened today, and how did you feel about them?

# [question
2](9780802483560_epub_toc_r1.htm#c02a)

What were some of your favorite toys as a child? What was your favorite candy?

# [question
3](9780802483560_epub_toc_r1.htm#c03a)

Describe the home of one or both sets of your grandparents.

# [question
4](9780802483560_epub_toc_r1.htm#c04a)

What was something you really wanted but were not allowed to own as a child or teen?

# [question
5](9780802483560_epub_toc_r1.htm#c05a)

Describe one of your favorite elementary school teachers. Then describe a favorite high school teacher or college professor.

# [question
6](9780802483560_epub_toc_r1.htm#c06a)

As you were growing up, what was unique about your family as compared to other families in your neighborhood or the families of your friends?

# [question
7](9780802483560_epub_toc_r1.htm#c07a)

What was your most serious physical injury as a child or teen?

# [question
8](9780802483560_epub_toc_r1.htm#c08a)

What do you remember about learning to drive?

# [question
9](9780802483560_epub_toc_r1.htm#c09a)

Can you recall visiting your parents' workplace?
If so, describe it and how you felt when you went there.

# [question
10](9780802483560_epub_toc_r1.htm#c10a)

Complete this sentence: "I'm sure my mom and dad wish I would...."

# [question
11](9780802483560_epub_toc_r1.htm#c11a)

What is perhaps the worst movie you have ever seen?

# [question
12](9780802483560_epub_toc_r1.htm#c12a)

What tragic news story in the last few years made you particularly sad?

# [question
13](9780802483560_epub_toc_r1.htm#c13a)

What was one of the most memorable weddings (other than your own) that you have attended?

# [question
14](9780802483560_epub_toc_r1.htm#c14a)

What is something you collected as a child or teen?

# [question
15](9780802483560_epub_toc_r1.htm#c15a)

What is a question that you wish you had the courage to ask your mother and/or father?

# [question
16](9780802483560_epub_toc_r1.htm#c16a)

If you were given five acres of land, where would you want it to be and what would you want to do with it?

# [question
17](9780802483560_epub_toc_r1.htm#c17a)

If you could own and operate your own business (and be guaranteed of its success), what would it be?

# [question
18](9780802483560_epub_toc_r1.htm#c18a)

If you do not play a musical instrument, what one do you wish you could play? If you do/did play a musical instrument, do you recall how you chose that particular one?

# [question
19](9780802483560_epub_toc_r1.htm#c19a)

What would you say are two of the best concerts you have seen, either in person or on video or film?

# [question
20](9780802483560_epub_toc_r1.htm#c20a)

What are two of your all-time favorite movies (or books)?

# [question
21](9780802483560_epub_toc_r1.htm#c21a)

My mother/father clearly did not understand what was considered cool when she/he bought me....

# [question
22](9780802483560_epub_toc_r1.htm#c22a)

What famous person (deceased) would you like to have met?

# [question
23](9780802483560_epub_toc_r1.htm#c23a)

Which of the following would you find most gratifying?

  earning a PhD

  publishing a bestselling book

  recording an original chart-topping song

  winning an Olympic gold medal

# [question
24](9780802483560_epub_toc_r1.htm#c24a)

If money and/or child care were no object, what would be your idea of the perfect New Year's Eve?

# [question
25](9780802483560_epub_toc_r1.htm#c25a)

Who is one of the most genuinely spiritual persons you know?

# [question
26](9780802483560_epub_toc_r1.htm#c26a)

How do you think the world has changed since September 11, 2001?

# [question
27](9780802483560_epub_toc_r1.htm#c27a)

Which of the following rides would be your first choice?

  a gondola in Venice

  a cab in London

  a Ferrari on the autobahn

  a hot air balloon in Switzerland

  an airboat in the Everglades

  a raft down the Colorado River

  a carriage in Paris

# [question
28](9780802483560_epub_toc_r1.htm#c28a)

What is one of your favorite memories that includes snow?

# [question
29](9780802483560_epub_toc_r1.htm#c29a)

Who was your favorite superhero or cartoon character?

# [question
30](9780802483560_epub_toc_r1.htm#c30a)

If someone could bless you and pass on to you a special ability,

who would you choose to bless you and with what ability?

# [question
31](9780802483560_epub_toc_r1.htm#c31a)

Who is the most joyful person you know?

# [question
32](9780802483560_epub_toc_r1.htm#c32a)

Who is someone you wish you could infect with a more positive attitude?

# [question
33](9780802483560_epub_toc_r1.htm#c33a)

Complete this sentence: "It would make me a better person if I were more like you in the way you...."

# [question
34](9780802483560_epub_toc_r1.htm#c34a)

When in your life would you say your self-esteem was the lowest?

# [question
35](9780802483560_epub_toc_r1.htm#c35a)

Recall a time when you were given constructive criticism that proved beneficial.

# [question
36](9780802483560_epub_toc_r1.htm#c36a)

The lion, beaver, otter, and golden retriever are used to describe four personality types. Which one do you think best describes you?

  Lion: strong, confident, leader, likes to make sure things get done

  Beaver: detail oriented, organized, follows instructions, good with projects

  Otter: very outgoing, enjoys people, humorous, creative

  Golden Retriever: loyal, sensitive, encouraging

# [question
37](9780802483560_epub_toc_r1.htm#c37a)

If you could hire Martha Stewart for a day, what would you have her do?

# [question
38](9780802483560_epub_toc_r1.htm#c38a)

Regardless of how long I live, I hope I will always....

# [question
39](9780802483560_epub_toc_r1.htm#c39a)

"It is more blessed to give than to receive." Recall a gift that gave you considerable satisfaction in presenting it.

# [question
40](9780802483560_epub_toc_r1.htm#c40a)

Describe the location and three features of your dream home.

# [question
41](9780802483560_epub_toc_r1.htm#c41a)

Who would you most like to hear one of the following from?

"I love you."

"I support you."

"I respect you."

"I appreciate you."

"I miss you."

"I trust you."

# [question
42](9780802483560_epub_toc_r1.htm#c42a)

In retrospect, what is something that your parents were wise in doing in raising you?

# [question
43](9780802483560_epub_toc_r1.htm#c43a)

What was your most/least favorite subject in school?

# [question
44](9780802483560_epub_toc_r1.htm#c44a)

If you could take a course in any subject right now at your local college, what type of course would it be?

# [question
45](9780802483560_epub_toc_r1.htm#c45a)

In what way are you most/least like your mother? How are you most/least like your father?

# [question
46](9780802483560_epub_toc_r1.htm#c46a)

In Matthew 6:34, Jesus encourages us to live with faith in the present.

Which is the greater obstacle for you?

  dwelling on the past

  worrying about the future

# [question
47](9780802483560_epub_toc_r1.htm#c47a)

Who was your best friend in junior high school? What did you do together?

# [question
48](9780802483560_epub_toc_r1.htm#c48a)

As I was growing up, my father was most like

  a coach   a judge

  an historian   a professor

  a preacher   a manager

  a cheerleader

  other______________

# [question
49](9780802483560_epub_toc_r1.htm#c49a)

If you inherited $200,000 (after taxes), what would you do with the money?

# [question
50](9780802483560_epub_toc_r1.htm#c50a)

Name three jobs or careers you are definitely not suited for.

# [question
51](9780802483560_epub_toc_r1.htm#c51a)

Describe your pediatrician when you were growing up.

What do you remember about those doctor visits?

# [question
52](9780802483560_epub_toc_r1.htm#c52a)

Concerning what biblical topic or Bible passage (or verse) do you wish you had a better understanding?

# [question
53](9780802483560_epub_toc_r1.htm#c53a)

What do you think that you will want to do in your retirement years?

# [question
54](9780802483560_epub_toc_r1.htm#c54a)

Acts 2:42-47 describes a close, caring community. In what setting have you had the greatest experience of genuine fellowship?

  friends at school   job where I worked   sports team

  support group   church-related group

  volunteer organization

  ministry/mission team   fraternity/sorority

  other______________

# [question
55](9780802483560_epub_toc_r1.htm#c55a)

What item of clothing in my wardrobe do you really like to see me wear?

# [question
56](9780802483560_epub_toc_r1.htm#c56a)

What is a song or piece of music that moves or inspires you?

# [question
57](9780802483560_epub_toc_r1.htm#c57a)

What quality or skill that you possess would you find most gratifying to have your child imitate as an adult?

# [question
58](9780802483560_epub_toc_r1.htm#c58a)

If you could win any competition in the world, what would it be?

# [question
59](9780802483560_epub_toc_r1.htm#c59a)

What nonbiblical historical event would you like to have witnessed?

# [question
60](9780802483560_epub_toc_r1.htm#c60a)

Name the Old Testament event that you wish you could have witnessed.
Name the New Testament event (in addition to the resurrection) that you wish you could have witnessed.

# [question
61](9780802483560_epub_toc_r1.htm#c61a)

In TV's _The Andy Griffith Show_,
Barney Fife once told Andy that the biggest purchase he ever made was a septic tank for his parents' wedding anniversary. What gift would you like to give to your parents?

# [question
62](9780802483560_epub_toc_r1.htm#c62a)

What is something you thoroughly enjoyed doing as a child and have not done in years?

# [question
63](9780802483560_epub_toc_r1.htm#c63a)

Richard Foster says that our lives are bombarded by hurry, crowds, and noise.
Which of those three has been most bothersome for you lately?

# [question
64](9780802483560_epub_toc_r1.htm#c64a)

Complete this sentence: "A time that I felt I might be in physical danger was when...."

# [question
65](9780802483560_epub_toc_r1.htm#c65a)

In what event would you most like to win an Olympic gold medal?

# [question
66](9780802483560_epub_toc_r1.htm#c66a)

Recall a time when you were disappointed in not being chosen.

# [question
67](9780802483560_epub_toc_r1.htm#c67a)

I wish I could hire ____________
to write and record a song from me to you.

# [question
68](9780802483560_epub_toc_r1.htm#c68a)

I think I would crack under the torture if I were forced to listen to only ____________ music all day and could only eat ____________ meals all day.

# [question
69](9780802483560_epub_toc_r1.htm#c69a)

If we were to adopt a child from another country, which country would it be?

# [question
70](9780802483560_epub_toc_r1.htm#c70a)

As a couple we make a great team, but it is most unlikely that we would ever team up to....

  win a mixed-doubles tennis championship

  sing a duet

  win a medal in couples figure skating

  be co-leaders (main speakers) of a nationally televised marriage seminar

  compete in a ballroom dancing competition

  operate a bed & breakfast

  co-author a book entitled _Stress-Free Parenting_

# [question
71](9780802483560_epub_toc_r1.htm#c71a)

Imagine that your internal dashboard has a spiritual passion gauge on it. What is your present reading?

E _______

¼ _______

½ _______

¾ _______

F _______

# [question
72](9780802483560_epub_toc_r1.htm#c72a)

What is the worst or most unusual job interview you ever had?

# [question
73](9780802483560_epub_toc_r1.htm#c73a)

The circus act that most reminds me of my job is....

# [question
74](9780802483560_epub_toc_r1.htm#c74a)

What is your most/least favorite trait in others?

# [question
75](9780802483560_epub_toc_r1.htm#c75a)

What kind of race best describes your last seven days?

  BOSTON MARATHON _It seemed to last forever_.

  TOUR DE FRANCE _I was pedaling uphill as fast as I could_.

  KENTUCKY DERBY _I worked for so long on something that was over so quickly_.

  INDIANAPOLIS 500 _I went round and round, and I'm right where I started_.

  IRONMAN TRIATHLON _I endured a week full of job, family, and church activities_.

  24 HOURS OF LE MANS _Sleep? What's that?_

  HUNDRED-METER HIGH HURDLES _All I did was sprint and navigate obstacles_.

  DEMOLITION DERBY _I feel beat up_.

# [question
76](9780802483560_epub_toc_r1.htm#c76a)

Talk about your early experiences with someone of another race or nationality.

# [question
77](9780802483560_epub_toc_r1.htm#c77a)

Describe a summer camp experience.

# [question
78](9780802483560_epub_toc_r1.htm#c78a)

What is your favorite animated film?

# [question
79](9780802483560_epub_toc_r1.htm#c79a)

When you were growing up, where did your family go on vacations? Describe one of those vacations.

# [question
80](9780802483560_epub_toc_r1.htm#c80a)

Can you remember a time when you got lost or separated from your family or companions? Describe what happened and how you felt.

# [question
81](9780802483560_epub_toc_r1.htm#c81a)

Recall a time when you got sick at a very inopportune time.

# [question
81](9780802483560_epub_toc_r1.htm#c81a)

What high school or college course would you rather flee the country than be forced to take again?

# [question
83](9780802483560_epub_toc_r1.htm#c83a)

Can you recall a first date during which you immediately knew there would not be a second date?

# [question
84](9780802483560_epub_toc_r1.htm#c84a)

Select and describe a couple who were friends with your parents when you were growing up.

# [question
85](9780802483560_epub_toc_r1.htm#c85a)

Recall a childhood memory about one of the following:

• playing in a creek

• playing in a tree house

• catching fireflies

• running a lemonade stand

• pretending to be a superhero

• a slumber party or sleepover

• jumping off the high dive

# [question
86](9780802483560_epub_toc_r1.htm#c86a)

Describe your parents' reaction on the day you moved out or left for college.

# [question
87](9780802483560_epub_toc_r1.htm#c87a)

Recall something special about your high school or college graduation.

# [question
88](9780802483560_epub_toc_r1.htm#c88a)

What is your favorite scene from your favorite movie?

# [question
89](9780802483560_epub_toc_r1.htm#c89a)

I thought it was one of the coolest items in my wardrobe at the time, but today I'm not sure I'd even wear it to a costume party. What is it?

# [question
90](9780802483560_epub_toc_r1.htm#c90a)

Something I wanted to quit but my parents wouldn't let me was....

# [question
91](9780802483560_epub_toc_r1.htm#c91a)

Joseph's brothers sold him into slavery. If you have siblings, what was one of the meanest things done to you by a brother or sister? If you are an only child, what was one of the meanest things done to you by a friend?

# [question
92](9780802483560_epub_toc_r1.htm#c92a)

Describe someone you encountered recently who probably needs God in his or her life.

# [question
93](9780802483560_epub_toc_r1.htm#c93a)

If you were offered the opportunity to be one of the contestants on _Survivor_, would you do it? If yes, what do you imagine would be the hardest thing for you to cope with?

# [question
94](9780802483560_epub_toc_r1.htm#c94a)

One of the descendants of King Saul was named Mephibosheth. Do you like your first name? If you could choose another first name for yourself, what would it be?

# [question
95](9780802483560_epub_toc_r1.htm#c95a)

In the movie _The Karate Kid_, young Daniel is befriended by an old Japanese man who teaches him karate, but more importantly offers him kindness and encouragement. Name an older person who blessed you with kindness and encouragement.

# [question
96](9780802483560_epub_toc_r1.htm#c96a)

What is one of your favorite stories that your parents tell about you?

# [question
97](9780802483560_epub_toc_r1.htm#c97a)

In the movie _Groundhog Day_, Bill Murray kept waking up only to repeat the same day over and over again. What recent day would you not want to repeat?

# [question
98](9780802483560_epub_toc_r1.htm#c98a)

What old photograph of yourself makes you really laugh or cringe in embarrassment?

# [question
99](9780802483560_epub_toc_r1.htm#c99a)

Recall something about exchanging valentines when you were in elementary school.

# [question
100](9780802483560_epub_toc_r1.htm#c100a)

Congratulations! Your boss just gave everyone a spring break. Where do you want to go?

# [question
101](9780802483560_epub_toc_r1.htm#c101a)

What is something that occurred this past year that you are especially thankful for?

# _More Resources on_
THE 5 LOVE LANGUAGES®

The 5 Love Languages®—Gift Edition
The 5 Love Languages®
The 5 Love Languages® Men's Edition

VISIT 5LOVELANGUAGES.COM

# _More Resources on_
THE 5 LOVE LANGUAGES®

The 5 Love Languages® of Children
The 5 Love Languages® of Teenagers
The Five Love Languages® Singles Edition
God Speaks Your Love Language

VISIT 5LOVELANGUAGES.COM

# _More Resources on_
THE 5 LOVE LANGUAGES®

Learning your love language—and that of your spouse, teen, and child—might be the easiest and most important thing you ever learn. The assessments featured at www.5lovelanguages.com make it easy to discover your love language. Simply take one of our short profiles and find out how you and your loved one express and interpret love.

Right away, you can make a concerted effort to speak his or her primary language. It might not come naturally, but even the effort will be appreciated.

This dynamic site is also full of other helpful features—links to other resources, free stuff, upcoming events, podcasts, video, and more—all designed to encourage you and strengthen your relationships. We want to help you feel loved, and to effectively communicate love to others.

**VISIT 5LOVELANGUAGES.COM**

What does the author say is the greatest obstacle for most people - dwelling on the past or worrying about the future?


Prompt:
Multi-Camera Multi-Object Tracking on the Move via
Single-Stage Global Association Approach

Pha Nguyen1

Kha Gia Quach

Chi Nhan Duong

Son Lam Phung

Ngan Le

Khoa Luu

Introduction

Object detection and tracking have become two of the most critical tasks in autonomous vehicles (AV). Recent developments in deep learning methods have dramatically boosted the performance of object understanding and tracking in autonomous driving applications.

Sample of multi-view captured via a multi-camera setup on a vehicle from nuScenes.

[fig:sample_nuscenes_a]

First row: the object detector KM3D fails to detect partial objects in one camera but can detect them in another. Second row: The detector fails to detect objects in both cameras. Third row: the SC-MOT method DEFT fragments a global object ID into many local IDs when it moves across cameras. The green arrow indicates the true-positive detection sample; the red arrows indicate false-negative detection and tracking samples.

[fig:sample_nuscenes_b]

The object tracking problem in AVs is far apart from multiple camera multiple object tracking (MC-MOT) in surveillance settings where cameras are stationary, i.e., their positions are fixed, but their poses may change in PTZ cameras cases. For clarity, MC-MOT in surveillance settings is referred to as static MC-MOT and MC-MOT in AVs as dynamic MC-MOT on-the-move since cameras are moving with the vehicle. Other works consider tracking the activities of people from multiple moving cameras, where the movements are subtle with large overlapping regions between cameras. In contrast, our camera setting in this paper contains large movements and small overlapping regions between cameras as the traveling car passes by other objects. Such a setup with some redundancy, i.e., certain overlapping fields-of-view, presents some new challenges for MOT to work with 3D object detectors to track objects and maintain the stability of predictions across video frames in multiple views.

As a result, camera-based tracking methods in the current leaderboard of autonomous driving datasets, e.g., nuScenes and Waymo, appear to be using only single-camera settings. However, the datasets were collected in multi-camera settings as shown in [fig:sample_nuscenes_a]. Thus, this work aims to use redundant data to improve detection and tracking performance.

In the MC-MOT settings, traditional two-stage approaches track objects on each camera independently, i.e., single-camera tracking (SCT), then link-local tracklets across cameras via global matching steps based on Re-ID features. Applying such a two-stage approach to dynamic MC-MOT settings on AVs leads to a problem with the global matching that relies on complicated graph structures to assign a global ID to all detection. In addition, this approach cannot handle scenarios when the detector fails to detect objects from one of the cameras. Moreover, it requires additional steps to merge many local IDs, as shown in Fig. [fig:sample_nuscenes_b]. Therefore, there are better solutions than using SCT multiple times.

Contributions of this Work

This work presents a single-stage MC-MOT approach directly using the outputs of an object detector as the inputs instead of SCT trajectories. To achieve this goal, we mathematically reformulate association steps in static MC-MOT into a single global association step as a one-to-many assignment problem to match one target, i.e., tracked objects in the world coordinate, with multiple detection, i.e., objects appear in multi-camera overlapping regions. This assignment can be solved efficiently via our proposed Fractional Optimal Transport Assignment (FOTA) method. Moreover, since this assignment problem can be defined in both the traditional track-by-detection scheme and the more recent track-by-attention scheme, we demonstrate its ability in both our proposed Single-Stage Global Assignment (SAGA) schemes as SAGA-Track and SAGA-TrackNet, respectively. Evaluate proposed methods with a comprehensive evaluation criterion to demonstrate their robustness compared to previous frameworks. The proposed method reduces the IDSwitch error from 3,807 to 870 and improves the tracking performance by up to 6.4% on the nuScenes Test Set benchmark.

Related Work

The MOT problem on AVs has recently received much attention in the research community. Recent methods in static MC-MOT settings have been reviewed in, while dynamic MC-MOT settings are still an open research area. The most recent work reviewed in this section is focused on the assignment or association formulation in SC-MOT and static MC-MOT.

Assignment in SC-MOT. While many works calculated the assignment costs between tracklets and detection by using some distance measurements over deep features or locations, some approaches directly computed the similarity scores. Xiang et al. built a bipartite graph over the affinity computed by the LSTM as edge cost and solved the association by the Hungarian algorithm. Ran et al. proposed a Pose-based Triple Stream Network to extract three kinds of similarity scores, i.e., appearance, motion, and interaction, and then fuse the average strategy into a final similarity score in a bipartite graph by the greedy match algorithm.

Assignment in Static MC-MOT. He et al. constructed a global similarity matrix from local tracklets in all single views and then estimated targets’ trajectory by offline performing Matrix Factorization. Ristani and Tomasi solved the ID assignment task by correlation clustering, then executed interpolation and elimination to fill the gap and filter indecisive tracks. Quach et al. proposed a dynamic graph to transform pre-computed Re-ID features into new context-aware ID features. Hence it performs better clustering and yields more accurate results. Yoon et al. maintained a set of track hypotheses all the time by the Multiple Hypothesis Tracking algorithms and also reduced the excess by introducing a gating mechanism for tree pruning. Zhang et al. utilized the Re-Ranking algorithm on the global cost matrix to cluster IDs. However, directly applying these approaches to the dynamic setting on AVs suffers from a significant real-time performance decrease, computation complexity, and domain irrelevance. Therefore, several methods to solve object tracking on the fly have been proposed, as referred to in the following parts.

Using Motion Models. Weng et al. proposed a simple yet effective baseline to utilize a classic state estimator (the Kalman Filter) for tracking 3D bounding boxes. These bounding boxes can be obtained from a point cloud object detector or an image-based object detector. Chiu et al. improved the Kalman Filter tracking system using the Mahalanobis distance between the predicted states and observations. The method is reasonably effective in filtering outliers and handling partially and fully occluded objects.

Using Appearance Models. Zhou et al.’s approaches are widely used for single-camera tracking. These approaches simplify the tracking procedure by treating objects as points, which usually involves many computationally intensive steps from detection to assigning object ID. Hu et al. estimated robust 3D box information from 2D images and adopted 3D box-reordering and LSTM as a motion module to link objects across frames.

Using Hybrid Approaches. Chaabane et al. trained the object detection and the object association task simultaneously by adding a feature extractor and a matching head after the object detector. In addition, an LSTM instead of a Kalman Filter is used for motion prediction. Yin et al. followed a similar process but performed feature extraction on point cloud maps.

Using Modern Approaches. Graph Neural Network, Self-Attention, and Transformer have led to a new learning-from-context paradigm. This paradigm has attracted considerable research attention recently because of its promising performance in a wide range of tasks from natural language processing to computer vision. A limited number of these methods have been applied for dynamic MC-MOT in autonomous vehicles, apart from many SC-MOT approaches. Weng et al. proposed the first feature interaction method that leverages a Graph Neural Network to adapt features from one object to another individual. Meinhardt et al. proposed a new tracking-by-attention paradigm (compared to the existing tracking-by-regression, tracking-by-detection, and tracking-by-segmentation) to deal with occlusions and determine the tracker’s Spatio-temporal correspondences. Sun et al. utilized the Query-Key mechanism to perform joint detection-and-tracking and disentangle complex components in previous tracking systems.

Compared to these prior works, the critical difference in our approach is that it uses a world coordinate system in AVs multi-camera system to solve the global one-to-many association step. It is possible by matching one tracked object with multiple detection. Thus, it eliminates the need for another association step, i.e., using Re-ID, and reduces the effort of adopting several empirical rules and heuristics to handle overlapping FOVs.

Our Proposed Method

This section presents the proposed dynamic MC-MOT approaches with a one-to-many global assignment method.

Problem Definition

Given video frames from $K$ cameras at the $t$-th time step, denoted by the set ${\mathcal{I}^{(t)}=\{I_1^{(t)},\dots, I_k^{(t)}\dots,I_K^{(t)}\}}$, MC-MOT system provides a set of detected objects $\mathcal{O}^{(t)} = \{ \mathbf{o}_{j}^{(t)}\}$ associated with their identities. Object bounding boxes and classes can be predicted using an object detector given each frame in $\mathcal{I}^{(t)}$ separately. The identities of objects are obtained by associating with tracklets, i.e., a set of bounding boxes with a track ID $i$ as $\mathcal{T}_i = \{ \mathbf{tr}^{(t_1)}_{i}, \mathbf{tr}^{(t_2)}_{i}, \cdots \}$. Objects detected on each camera and track are represented by 3D bounding boxes in world coordinates. Note that tracklets are shared across cameras and are often referred to as a global track ID. During $T$ frames of a video sequence, the sub-sequence of $(t_1, t_2, \cdots)$ is the time steps when the tracked object appears within the camera views. Each track $\mathbf{tr}^{(t)}_{i}$ is estimated using a motion model from the previous frame $t-1$ and then updated with the detection of the corresponding tracked objects as follows, $$\footnotesize \begin{split} \hat{\mathbf{tr}}^{(t)}_{i} &= \mathcal{M}_{\text{pred}} (\mathbf{tr}^{(t - 1)}_{i}) \\ \mathbf{tr}^{(t)}_{i} &= \mathcal{M}_{\text{update}} (\hat{\mathbf{tr}}^{(t)}_{i}, \mathbf{o}^{(t)}[i]) \end{split}$$ $$\footnotesize \text{where } \mathbf{o}^{(t)} [i] = \begin{cases} \mathbf{o}^{(t)}_{j} & \text{if detected object } \mathbf{o}^{(t)}_{j} \text{ associates with the $i$-th tracklet}\\ \varnothing & \text{if no object } \text{ associates with the $i$-th tracklet} \end{cases}$$ Here, $\mathcal{M}_{\text{pred}}$ is a function or a network to predict the following location of the track based on the motion model, and $\mathcal{M}_{\text{update}}$ is a function to update the location of the track in the current time step $t$. In this paper, we use two different motion models, i.e., linear Kalman Filter and Non-linear Transfomer-based Network. To know which detected object $j$ is being used to update the corresponding tracklet $i$. Each detection is then assigned to a tracklet based on a matching algorithm with a cost function. It also determines whether the detection is a new or existing object from the previous frame. Generally, the cost functions to match detection with tracklets can be defined as in Eqn. [eq:cijk]. $$\label{eq:cijk} \footnotesize c_{ij} = \mathcal{C}_{\text{match}} [i, j] =d\left(\hat{\mathbf{tr}}^{(t)}_{i}, \mathbf{o}_{j}^{(t)} \right)$$ where $d(\cdot, \cdot)$ is the distance between the detected and tracked objects. Several distance metrics can be adopted for $d(\cdot, \cdot)$ such as Mahalanobis distance implemented in, 2D or 3D GIoU.

$$\label{eq:mahalanobis} \footnotesize d_{\text{Mahalanobis}} \left(\hat{\mathbf{tr}}^{(t)}_{i}, \mathbf{o}_{j}^{(t)} \right) = \sqrt{(\mathbf{o}_{j}^{(t)} - \hat{\mathbf{tr}}^{(t)}_{i})^{T} {\mathbf{S}^{(t)}}^{-1} (\mathbf{o}_{j}^{(t)} - \hat{\mathbf{tr}}^{(t)}_{i})},$$ $$\label{eq:g_iou} \footnotesize d_{\text{GIoU}} \left(\hat{\mathbf{tr}}^{(t)}_{i}, \mathbf{o}_{j}^{(t)} \right) = 1 - \left(\frac{|\mathbf{o}_{j}^{(t)} \cap \hat{\mathbf{tr}}^{(t)}_{i}|}{|\mathbf{o}_{j}^{(t)} \cup \hat{\mathbf{tr}}^{(t)}_{i}|} - \frac{|\mathbf{cv}^{(t)}_{ij} \setminus (\mathbf{o}_{j}^{(t)} \cup \hat{\mathbf{tr}}^{(t)}_{i})|}{|\mathbf{cv}^{(t)}_{ij}|}\right),$$

Here, $\mathbf{S}^{(t)}$ is the covariance that represents the uncertainty of the predicted object state as implemented in. In addition, $\mathbf{cv}^{(t)}_{ij}$ is the smallest enclosing convex shape of $\mathbf{o}_{j}^{(t)}$ and $\hat{\mathbf{tr}}^{(t)}_{i}$. Note that multi-view geometry is implicitly applied when we compute the distance metrics above.

Single-Stage Global Assignment Tracking Approach (SAGA-Track)

With the cost matrix defined above, the assignment algorithm has to assign the detected objects to the correct tracklets. To assign detection to tracklets, a straightforward approach is to pool all detection and tracks into two corresponding sets and perform an one-to-one matching algorithm, i.e., Hungarian algorithm, based on a cost matrix similar to the SC-MOT case. However, in dynamic MC-MOT settings, one object can appear in several cameras simultaneously due to camera overlapping. That means two or more detected objects $\mathbf{o}^{(t)}_{j}$ in different cameras should be matched to one tracklet $\hat{\mathbf{tr}}^{(t)}_{i}$ only. Therefore, the one-to-one matching algorithm cannot handle detection from multiple cameras put together as only one instance of an object in a camera can be matched to the target tracklet causing the remaining detection of that object in other cameras to be unmatched. These unmatched instances may create new tracklets during the tracking process, and a second association step is needed to connect them. It is referred to as the global baseline association in our experiments.

To further equip a tracking system with the capability of tracking multiple instances of the same objects in different cameras, we propose to cast this assignment process to a distribution matching task where tracklets and all detected objects at $t$-th time step can be formed into two distributions. Formally, let $\mathcal{X} = \{ \hat{\mathbf{tr}}^{(t)}_{i}\}_{i=1}^N$ and $\mathcal{Y} = \{ \mathbf{o}^{(t)}_j\}_{j=1}^M$ be the sets of $N$ current tracklets and $M$ detected objects from all $K$ cameras at the $t$-th time step. Let $\mathbf{p}$ and $\mathbf{q}$ be the empirical distributions defined over $\mathcal{X}$ and $\mathcal{Y}$, respectively. The set of all possible couplings $\Pi (\mathbf{p}, \mathbf{q})$ to transport the mass, i.e., number of object entities, from $\mathcal{X}$ to $\mathcal{Y}$ is defined as in Eqn. [eqn:TrackletObjCoupling]. $$\label{eqn:TrackletObjCoupling} \footnotesize \Pi (\mathbf{p}, \mathbf{q}) = \begin{cases} & \boldsymbol{\pi} \in \mathbb{R}_+^{|\mathbf{p}| \times |\mathbf{q}|}: \\ & \boldsymbol{\pi}\mathbb{1}_{|\mathbf{q}|} \leq \mathbf{p}, \boldsymbol{\pi}^{\top}\mathbb{1}_{|\mathbf{p}|} \leq \mathbf{q},\mathbb{1}_{|\mathbf{p}|}^{\top} \boldsymbol{\pi} \mathbb{1}_{|\mathbf{q}|} = s \end{cases} \Bigg\}$$ where $\pi_{ij}$ denotes the amount of a mass $p_i$ at $\hat{\mathbf{tr}}^{(t)}_{i}$ being associated with the mass $q_j$ at $\mathbf{o}^{(t)}_j$. The inequality in Eqn. [eqn:TrackletObjCoupling] indicates the possibility of fractional entities being matched between the two distributions as (1) one tracklet in $\mathcal{X}$ can associate with no detection (i.e., the tracked object does not appear in all cameras) or many detections (i.e., the tracked object appears in many cameras); and (2) one detection in $\mathcal{Y}$ can be assigned to zero or one tracklet in $\mathcal{X}$. Moreover, different from the standard Optimal Transport (OT) based approach where the two distributions are required to have the same total probability mass, i.e., $||\mathbf{p}||_1 = ||\mathbf{q}||_1$, and all the mass has to be transported. Eqn. [eqn:TrackletObjCoupling] focuses on transporting only a fraction $s$ of the mass between two distributions. Thus, we named this approach as Fractional OT Assignment (FOTA).

Let $\mathbf{C}=(c_{i,j})$ be the transportation cost matrix where $c_{i,j}$ measures a cost to associate from $\hat{\mathbf{tr}}^{(t)}_{i}$ to $\mathbf{o}^{(t)}_j$. The proposed FOTA addresses the problem of finding the best assignment solution $\pi$ that minimizes the transportation cost between two distributions: $$\label{eq:optimal_transport} \footnotesize \min_{\boldsymbol{\pi} \in \Pi(\mathbf{p}, \mathbf{q})} \langle \mathbf{C}, \boldsymbol{\pi}\rangle_F = \min_{\boldsymbol{\pi} \in \Pi(\mathbf{p}, \mathbf{q})} \sum_{i}^N \sum_{j}^M c_{ij}\pi_{ij}$$ To address the constraints of only transporting a fraction $s$ of mass in Eqn. [eqn:TrackletObjCoupling], we propose attaching one more row and column in the cost matrix to handle the mass difference between two distributions as in Eqn. [eq:extend_cost]. $$\label{eq:extend_cost} \footnotesize \bar{\mathbf{C}} = \begin{bmatrix} \mathbf{C} & \mathcal{E} \mathbb{1}_{|\mathbf{q}|} \\ \mathcal{E} \mathbb{1}_{|\mathbf{p}|}^{\top} & 2\mathcal{E} + \max(\mathbf{C}) \\ \end{bmatrix}$$ where $\mathcal{E}$ is a scalar for the bound. If we set the mass of the additional track and object as $p_{N+1} = \| \mathbf{q} \|_1 - s$ and $q_{M+1} = \| \mathbf{p} \|_1 - s$, finding the best assignment solution $\pi$ can be reduced to an unconstrained problem $\min_{\bar{\boldsymbol{\pi}} \in \Pi(\bar{\mathbf{p}}, \bar{\mathbf{q}})} \langle \bar{\mathbf{C}}, \bar{ \boldsymbol{\pi}} \rangle_F$, where $\bar{\mathbf{p}} = [\mathbf{p}, \| \mathbf{q} \|_1 - s]$ and ${\bar{\mathbf{q}} = [\mathbf{q}, \| \mathbf{p} \|_1 - s]}$.

Solving the One-to-many Assignment. From the above formula for the Optimal Transport-based Assignment problem in Eqn. [eq:optimal_transport], one can solve it in polynomial time as it is a linear program. However, when there are multiple detected objects and tracklets, the resulting linear program can be large. This issue can be addressed by a fast iterative solution named Sinkhorn-Knopp, which converts the optimization target in Eqn. [eq:optimal_transport] into a non-linear but convex form by adding a regularization term $E$ as in Eqn. [eq:ot_reg]. $$\label{eq:ot_reg} \footnotesize \min_{\bar{\boldsymbol{\pi}} \in \Pi(\bar{\mathbf{p}}, \bar{\mathbf{q}})} \sum_{i}^N \sum_{j}^M c_{ij} \pi_{ij} + \gamma E\left( \bar{\pi}_{ij} \right)$$ where $E( \bar{\pi}_{ij} ) = \bar{\pi}_{ij} ( \log(\bar{\pi}_{ij}) - 1)$. Here, $\gamma$ is a constant regularization term. The constraint optimization target in Eqn. [eq:ot_reg] can be converted to a non-constraint target using the Lagrange Multiplier method as in Eqn. [eq:Lagrange]. $$\begin{aligned} \label{eq:Lagrange} \footnotesize \min_{\boldsymbol{\bar{\pi}} \in \Pi(\bar{\mathbf{p}}, \bar{\mathbf{q}})} \sum_{i}^N \sum_{j}^M c_{ij} \bar{\pi}_{ij} + \gamma E\left( \bar{\pi}_{ij} \right) + \alpha_j \left( \boldsymbol{\bar{\pi}}^{\top}\mathbb{1}_{|\bar{\mathbf{p}}|} - \bar{\mathbf{q}} \right) + \beta_i \left( \boldsymbol{\bar{\pi}}\mathbb{1}_{|\bar{\mathbf{q}}|} - \bar{\mathbf{p}} \right)\end{aligned}$$ where $\alpha_j(j = 1,2,...M)$ and $\beta_i(i = 1,2,...,N)$ are Lagrange multipliers. By letting the derivatives of the optimization target equal 0, the optimal plan $\boldsymbol{\bar{\pi}}^{\star}$ is resolved as: $$\footnotesize \bar{\pi}^{\star}_{ij} = \exp \left( -\frac{\alpha_j}{\gamma} \right) \exp \left(-\frac{c_{ij}}{\gamma} \right) \exp \left( -\frac{\beta_i}{\gamma} \right)$$ Let $u_j = \exp \left( -\frac{\alpha_j}{\gamma} \right), v_i = \exp \left( -\frac{\beta_i}{\gamma} \right), \mathbf{W}[i,j] = \exp \left(-\frac{c[i,j]}{\gamma} \right)$, the following constraints can be enforced: $$\footnotesize \sum_i \bar{\pi}_{ij} = u_j \left( \sum_i \mathbf{W}[i,j] v_i \right) = \| \bar{\mathbf{q}} \|_1$$ $$\footnotesize \sum_j \bar{\pi}_{ij} = \left( u_j \sum_i \mathbf{W}[i,j] \right) v_i = \| \bar{\mathbf{p}} \|_1$$ To constraint these two equations simultaneously, one can calculate $v_i$ and $u_j$ by alternately updating the following: $$\label{eq:sinkhorn_iteration} \footnotesize u_j^{t+1} = \frac{\| \bar{\mathbf{q}} \|_1}{\sum_i \mathbf{W}[i, j] v_i^t}, v_i^{t+1} = \frac{ \| \bar{\mathbf{p}} \|_1}{\sum_j \mathbf{W}[i, j] u_j^{t+1}}$$ Eqn. [eq:sinkhorn_iteration] is also known as the Sinkhorn-Knopp Iteration updating equations. After repeating this iteration $T$ times, the approximate optimal plan $\boldsymbol{\bar{\pi}}^\star$ can be obtained: $$\label{eq:sinkhorn_iteration_final} \footnotesize \boldsymbol{\bar{\pi}}^\star = \text{diag}(v) \mathbf{W} \text{ diag}(u)$$ where $\gamma$ and $T$ are empirically set to 0.1 and 50.

[fig:saga_track]

In summary, as shown in Fig. [fig:saga_track], SAGA-Track with a multi-camera matching algorithm is performed in the following steps:

 1. Estimating the next location of the track $\mathbf{tr}_i^{(t-1)}$ using motion model, e.g., Kalman filter.

 2. Computing world-coordinate-based distance metrics between $\hat{\mathbf{tr}}_i^{(t)}$ and $\mathbf{o}_{j}^{(t)}$.

 3. Solving One-to-many FOTA assignments as in Eqn. [eq:sinkhorn_iteration_final].

 4. Updating $i$-th tracklet $\mathbf{tr}_i^{(t)}$ based on assigned objects.

In addition to the proposed track-by-detection scheme for multi-camera, we introduce a novel end-to-end framework including detector, motion model, tracker, and assignment steps in a single model in the next section 3.3. This end-to-end framework can be fully aware of objects’ movement globally rather than taking pre-computed detection as SAGA-Track.

End-to-end Learning MC-MOT via FOTA Loss

In this section, we further leverage the proposed FOTA into the design of the end-to-end learning network for MC-MOT, named SAGA-TrackNet.

Our proposed architecture consists of an encoder, two decoders, and a box-matching layer. The one-to-many assignment algorithm is implemented to provide the final tracking results from detection and tracked boxes as in Fig. [fig:framework].

Model Structure

The SAGA-TrackNet structure is based on transformer encoder, and decoder tracking frameworks and contains multi-head attention layers. These layers can be self-attention or cross-attention, i.e., keys and queries are the same or different.

Encoder. Features of the current and previous frame from a camera are extracted by a backbone CNN network, e.g., Resnet-50, and stacked together with other cameras. Features of the previous frame were saved to avoid re-computation. The encoder of SAGA-TrackNet then encodes those feature maps into keys for being used in the following decoders.

Object Decoder. To detect new objects on each camera, the model takes multiple sets of learnable parameters, named object queries, as a set of objects of interest in the images to match with keys, i.e., the feature maps generated by the encoder, and provides the outputs as "detected boxes."

Track Decoder and Matching. Simultaneously, the model takes tracked objects in the previous frames as the track query to infer the location of the corresponding tracked objects in the current frame and provides "tracked boxes." It is performed using the decoder block as it learns object motion similar to the Kalman filter. We can also utilize this Track Decoder block as a motion model to refine any off-the-shelf 3D object detectors by treating the track queries as placeholders and feeding detector predictions to this block. The motion modeling ablative study is further discussed in Subsection 4.3. During testing, the matching layer then performs the association of detected objects and tracked objects via FOTA. During training, a set prediction loss is computed for all $M + N$ output predictions in two steps: (a) loss for detecting object at frame $t - 1$ using $M$ object queries; (b) loss for tracking objects from (a) and detecting new objects at frame $t$ with all $M$ object queries and $N$ track queries from the frame $t - 1$. This prediction loss, computed based on the assignment obtained from FOTA between ground truth and prediction, is described in the following Subsection 3.3.2.

Model Training

This section presents the procedure for training our proposed end-to-end learning networks. Training Data. We train our proposed SAGA-TrackNet on a large-scale dataset, i.e., nuScenes, a training set with 750 scenes of 20s each, and use its validation set for our ablation study. Each training sample contains a chunk size of two consecutive frames from a training sequence.

FOTA Loss for Modeling Training. To compute this loss function, we also need to compute the assignment $\pi_{ij}$ between one of the ground-truth tracks $\mathcal{T}^{\star}_i$ or background to the joint set of object and track query predictions $\hat{\mathbf{o}}_j^{(t)}$. Similar to the OT-based assignment described in Subsection 3.2, the assignment is computed based cost matrix using a pre-defined distance between bounding boxes. Let us denote $G^{(t)} \subset G$ as the subset of ground-truth track ID at time step $t$. Then we assign each detection from step (a) to its corresponding ground-truth track ID $i$ from the set $G^{(t-1)} \subset G$. These two sets are explicitly assigned to the ground-truth objects in frame $t$ as $G^{(t)} \cap G^{(t-1)}$. Another set of ground-truth track ID is $G^{(t)} \backslash G^{(t-1)}$, which includes tracks not visible at time $t$. The last set is the new object not yet being tracked ground-truth objects, i.e., new objects, as $G^{(t - 1)} \backslash G^{(t)}$ to be matched with $M$ object queries.

$$\footnotesize \min_{\bar{\boldsymbol{\pi}} \in \Pi(\bar{\mathbf{p}}, \bar{\mathbf{q}})} \langle \bar{\mathbf{C}}, \bar{ \boldsymbol{\pi}} \rangle_F = \underset{\bar{\boldsymbol{\pi}} \in \Pi(\bar{\mathbf{p}}, \bar{\mathbf{q}})}{\min} \overset{N}{\underset{i=1}{\sum}} \overset{M}{\underset{j=1}{\sum}} c_{ij} \pi_{ij}$$ Using a similar extension as in Eqn. [eq:extend_cost], the cost matrix $\mathbf{C}$ can now be defined as in Eqn. [eq:CC]. $$\label{eq:CC} \footnotesize \mathbf{C} = ( c_{ij} ) = -\hat{p}_{\pi_{ij}}(\text{cls}_i) + \mathcal{C}_{\text{box}} \left(\mathcal{T}_{i}^{\star(t)}, \hat{\mathbf{o}}^{(t)}_{j} \right)$$ where $\text{cls}_i$ is the class id of the object and $\mathcal{C}_{\text{box}}$ term penalizes bounding box differences by a linear combination of a $\ell_1$ distance and a Generalized Intersection over Union as defined in Eqn. [eq:g_iou], $$\footnotesize \mathcal{C}_{\text{box}} = \lambda_{\ell_1} \| \mathcal{T}_{i}^{\star(t)} - \hat{\mathbf{o}}^{(t)}_{j} \|_1 + \lambda_{GIoU} \mathcal{C}_{GIoU} \left(\mathcal{T}_{i}^{\star(t)}, \hat{\mathbf{o}}^{(t)}_{j} \right)$$ We use set prediction loss to measure the set of predictions for $M$ detection and $N$ tracklets compared with ground-truth tracks in terms of classification and location (bounding boxes). Set-based loss is based on the optimal bipartite matching (described in Sections 3.2 and 3.3) between $M$ detection and ground-truth objects while $N$ tracklets will be matched with boxes from previous frames. The final MC-MOT set prediction loss is defined as in Eqn. [eq:cbox]. $$\label{eq:cbox} \small \mathcal{L}_{\text{MC-MOT}} (\mathcal{T}^{\star}, \hat{\mathbf{o}}^{(t)}, \boldsymbol{\pi}) = \overset{M + N}{\underset{j=1}{\sum}} \mathcal{L}_{\text{query}} (\mathcal{T}^{\star}, \hat{\mathbf{o}}_j^{(t)}, \boldsymbol{\pi})$$ The output predictions that do not match any ground-truth tracks will be assigned to the background class $\text{cls}_i = 0$. We indicate the ground-truth track matched with prediction $i$ by $\pi_{ij} = 1$ and define the loss per query as in Eqn. [eq:lquery]. $$\label{eq:lquery} \footnotesize \mathcal{L}_{\text{query}} \left( \mathcal{T}^{\star}, \hat{\mathbf{o}}_j^{(t)}, \boldsymbol{\pi} \right) = \begin{cases} -\hat{p}_{\pi_{ij}}(\text{cls}_i) +\mathcal{L}_{\text{box}} \left(\mathcal{T}_{i}^{\star(t)}, \hat{\mathbf{o}}^{(t)}_{j} \right) & \text{if } \pi_{ij} = 1 \\ -\hat{p}_{\pi_{ij}}(0) & \text{if } \pi_{ij} = 0 \\ \end{cases}$$ where $\mathcal{L}_{box}$ is the combination of the $\ell_1$ loss and the generalized Intersection over Union (IoU) for 3D boxes.

Model Inference

During testing, SAGA-TrackNet performs feature encoding, object decoding, and track decoding, then one-to-many matching for two consecutive frames from all cameras. The output features from the backbone network are stored in combination with the subsequent frames. We also keep tracked objects alive and allow them to rebirth to handle occlusion or disappearing quickly.

Experimental Results

In this section, we detail the benchmark datasets and metrics in Subsection 4.1. Then, the setups for all experiments and the ablation study will be presented in Subsection 4.2 and 4.3 respectively. The comparisons with the SOTA methods will be detailed in Subsection 4.4 on a large-scale Tracking Challenge, i.e. nuScenes Vision Track.

Benchmark Datasets and Metrics

nuScenes2 Dataset is one of the large-scale datasets for Autonomous Driving with 3D object annotations. It contains 1,000 videos of 20-second shots in a setup of 6 cameras, i.e. 3 front and 3 rear ones, with a total of 1.4M images. It also provides 1.4M manually annotated 3D bounding boxes of 23 object classes based on LiDAR data. This dataset has an official split of 700, 150, and 150 videos for training, validation, and testing, respectively.

The proposed method is evaluated using both detection and tracking metrics described in.

Detection Metrics. A commonly used metric, i.e. Mean Average Precision (mAP), is defined as a match using a 2D center distance on the ground plane instead of intersection over union cost for nuScenes detection challenges.

Similarly, other motion-related metrics are also defined in nuScenes, such as Average Translation Error (ATE) measuring Euclidean center distance in 2D in meters, Average Scale Error (ASE) computing as $1 - IOU$ after aligning centers and orientation, Average Orientation Error (AOE) measuring the smallest yaw angle difference between prediction and ground-truth in radians, Average Velocity Error (AVE) measuring the absolute velocity error in $m/s$ and Average Attribute Error (AAE) computing $1 - acc$, where $acc$ is the attribute classification accuracy. We also use the nuScenes Detection Score (NDS) that is based on a simple additive weighting of the mean of all metrics above.

Tracking Metrics. The tracking performance is measured using the popular CLEAR MOT metrics including MOTA, MOTP, ID switch (IDS), mostly tracked (MT), mostly lost (ML), fragmented (FRAG). Similar to nuScenes, we use two accumulated metrics introduced in as the main metrics, including the average over the MOTA metric (Average MOTA (AMOTA)) and the average over the MOTP metric (Average MOTP (AMOTP)).

Experiments Setup

[tab:nuscene_detection_results]

The proposed SAGA-TrackNet is trained with two consecutive frames where the extracted features in the previous time step $t-1$ are stored and stacked with the features of the current time step to encode object key features to predict the location of new and existing objects at time step $t$. Then, Mini-batch (chunk of two) gradient descent is employed with an Adam optimizer to learn all the parameters in the attention layers. All the layers and algorithms are implemented in PyTorch, based on Trackformer, TransTrack and Deformable DETR. The best configuration of layers is chosen empirically as three stacking self-attention layers with four heads and three stacking cross-attention layers with 16 heads. With a batch size of 512 chunks, the model converged at about 100 epochs.

Ablation Study

In this section, we present some experiments to ablate the effect of each component of the proposed framework. Particularly, this section aims to demonstrate the following: 1. how this motion modeling can help improve 3D object detectors; 2. better motion modeling with track decoder layers in SAGA-TrackNet; 3. how the combination of the external input and data association method affects the tracking performance. We also compare the processing time of these methods as well as the end-to-end solution.

[tab:motion_errors]

[tab:association_ablation_study]

Improving 3D Object Detector. Table [tab:nuscene_detection_results] demonstrates that the combination of baselines object detector and our motion model (i.e. the Track Decoder) achieves better results than the original detector. In this experiment, we initialize detected objects at previous frames as inputs to the track queries. The Track Decoder takes that set of objects and then combines it with frame features produced by the Encoder to refine the location of pseudo-"tracked boxes". The best result is achieved with the combination of KM3D object detector and our motion model since it is guided by decoded locations from our transformation procedure as described in Section 3.3.

The Role of Motion Model. Motion models are particularly essential in dynamic MC-MOT settings since cameras are moving with the vehicle. In this experiment, we evaluate the effectiveness of different motion modeling methods on detection performance. We use the locations predicted by motion models to compare with ground-truth locations in terms of motion-related metrics. In such a way, we can evaluate how well the motion model captures and predicts the motion of tracked objects. We compare with two other commonly used motion models, i.e. 3D Kalman Filter and LSTM. As shown in Table [tab:motion_errors], our SAGA-TrackNet gives better results than a classical object state prediction technique, i.e. 3D Kalman Filter used in and a deep learning-based technique, i.e. LSTM module, used in.

[tab:track_ablation_study]

Comparison of Different Distance Cost and Matching Algorithms. The proposed assignment module operates on a global cost matrix, which is computed from a detection set and a track set by different types of distance, i.e. Mahalanobis distance as defined in Eqn. [eq:mahalanobis], Bird’s Eye View 2D, and 3D bounding box GIoU as defined in Eqn. [eq:g_iou] between the estimated object states and the detected object bounding boxes. Then, the Sinkhorn iterative is employed as described in Section 3.2 with the maximum number of iterations being 100. Compared to Kuhn-Munkres (KM) algorithm, our framework inherits the merit of one-to-many matching and yields better results on assignment metrics and tracking metrics with a slight increase in computation cost in distance matrices construction and optimization (shown in Table [tab:association_ablation_study]). The performance of our proposed FOTA algorithm is also better than other tracklet-detection matching methods as shown in Table [tab:track_ablation_study].

[fig:compare_tracking]

[tab:nuscene_val_track_results]

[tab:nuscene_test_track_results]

Comparison against State-of-the-Art Methods

In this section, we compare our proposed framework with other vision-based (without using LiDAR or RADAR information) tracking approaches, which are the top in nuScenes vision only tracking challenge leaderboard.

Comparison against Tracking Methods on Validation set. This experiment compares our proposed method with other vision-based methods, including QD-3DT, MonoDIS + AB3DMOT, CenterTrack, and DEFT which are the tops of nuScenes vision-only tracking challenge. As we can see in Table [tab:nuscene_val_track_results], we outperform the top approach, i.e. QD-3DT, in most of the metrics. Fig. [fig:compare_tracking] illustrates the key factor that helps improve the tracking performance: we perform appearance matching across cameras in addition to motion modeling. It shows that our proposed method (top) can assign object ID globally between cameras compared with DEFT (bottom). Our method beats the SOTA method, i.e. QD-3DT on most of the main metrics, such as AMOTA, AMOTP, MOTAR, MOTA, Recall, IDSwitch, and FRAG, which are related to how well our method groups tracklet IDs and regresses object’s bounding boxes. For fair comparison and to preserve the originality and uniqueness of those methods, such as the LSTM motion model of DEFT, the offset head of CenterTrack, we implement a simple global association as the baseline, which takes MOT output results from those approaches and then adopts several empirical rules and heuristics to determine and filter out duplicated objects, including IOU thresholding and box merging as similar to ELECTRICITY.

Comparison against Tracking Methods on Test set. We submitted our result on the official competition platform EvalAI3. As it can be referred to the tracking challenge leaderboard on Vision track at nuScenes’ homepage4 and Table [tab:nuscene_test_track_results], our method performs better than QD-3DT and DEFT significantly on IDS (870 vs. 6,856 and 6,901) and slightly on AMOTA (0.242 vs. 0.217 and 0.177), this behavior is similar to the validation results in Table [tab:nuscene_val_track_results].

Conclusions

This paper has introduced a new global association approach to solving the dynamic MC-MOT problem for AV. The proposed framework can learn how to perform tracking frame-by-frame in an end-to-end manner given frames from multi-camera to extract features, encode object key features, decode new objects’ locations, decode tracked objects’ locations, and global association tracklets with detection. These tasks are enhanced with self-attention and cross-attention layers to capture structures and motion across cameras. The experiments have shown performance improvements up to 6.4% and a decrease in IDSwitch error from 3,807 to 870 in a large-scale AV dataset regarding vision-based detection and tracking accuracy.

 1. [note]equal contribution↩

 2. License CC BY-NC-SA 4.0↩

 3. https://eval.ai/web/challenges/challenge-page/476/leaderboard/1321↩

 4. https://www.nuscenes.org/tracking/↩

文章提出的端到端学习网络SAGA-TrackNet由哪几个组件组成?这些组件间是如何协同工作的?


Prompt:
Select me job titles from html and wite them down as CSV. Html data  /A>

Prace a Nabidka prace

1 - 20 z 3 322

Pohodova prace na 12ctkach - Kratky/dlouhy tyden
Job Leader EuropeZatec, Cesko<svg><use></use></svg>25 000 - 27 000 Kc/mesic<svg><use></use></svg>Reagujte bez zivotopisu

Pridanepred 1 minutou

<svg><use></use></svg>

Nocni smeny ve vyrobe | Modrice CTPark |
Gi Group Czech Republic, s.r.o.Modrice<svg><use></use></svg>25 000 - 26 000 Kc/mesic

Pridanepred 1 minutou

<svg><use></use></svg>

Operator/pomocni ve skladu v Nemecku bez znalosti jazyka
MD Personalservice GmbHNemecko<svg><use></use></svg>2 200 - 2 600 EUR/mesic<svg><use></use></svg>Reagujte bez zivotopisu

Pridanepred 7 minutami

<svg><use></use></svg>

Hleda se personalni konzultant/ka! 30 000 - 40 000 Kc
Gi Group Czech Republic, s.r.o.Brno<svg><use></use></svg>30 000 - 40 000 Kc/mesic

Pridanepred 7 minutami

<svg><use></use></svg>

Vyvojar pre Windows Desktop, WPF, C#.
Solver IT s.r.o.Zilina, Slovensko<svg><use></use></svg>1 800 - 2 600 EUR/mesic<svg><use></use></svg>Mozhlivist' dlia liudei z Ukrayini

Pridanepred 16 minutami

<svg><use></use></svg>

Vykonny riaditel / Financny riaditel / Obchodny riaditel - Managing Director / Chief Financial Officer / Commercial Director (f/m/x)
Medialink Communications Consult s.r.o.Bratislava, Slovensko<svg><use></use></svg>7 000 EUR/mesic</SP


Prompt:
Holdings Data: 'Holding, Number of shares, NAME_1 price/ Average price per share ($), Client investment ($), Cost basis ($), Price per share on Feb 28 ($), Value on Feb 28 ($), Unrealized (tax) gain or loss ($), Investment return ($), Holding period
FEDERATED HERMES, , , , , , , , ,
STRATEGIC VALUE DIVIDEND, , , , , , , , ,
FUND IS, , , , , , , , ,
Symbol: SVAIX, , , , , , , , ,
Trade date: Feb 4 21, 1366.279, 5.160, 7050.00, 7050.00, 6.110, 8347.96, 1297.96, , LT
Total reinvested, 57.410, 5.678, , 326.02, 6.110, 350.77, 24.75, ,
EAI: $313 Current yield: 3.60% Security total, 1423.689, 5.181, 7050.00, 7376.02, , 8698.73, 1322.71, 1648.73,
O'SHAUGHNESSY MARKET LEADERS VALUE FUND CLASS, , , , , , , , ,
I, , , , , , , , ,
Symbol: OFVIX, , , , , , , , ,
Trade date: Feb 4 21, 470.628, 14.979, 7050.00, 7050.00, 17.710, 8334.81, 1284.81, , LT
Total reinvested, 8.717, 17.859, , 155.68, 17.710, 154.38, -1.30, ,
EAI: $159 Current yield: 1.87%, , , , , , , , ,
Security total, 479.345, 15.032, 7050.00, 7205.68, , 8489.19, 1283.51, 1439.19,
PACE portfolio total, , , $14100.00, $14581.70, , $17187.92, $2606.22, $3087.92,
NAME_2 MULTI-STRATEGY, , , , , , , , ,
INCOME FUND CLASS INSTL, , , , , , , , ,
Symbol: ANGIX, , , , , , , , ,
Trade date: Sep 23 20, 2408.841, 10.179, 24522.00, 24522.00, 10.110, 24353.38, -168.62, , LT
Total reinvested, 155.558, 10.351, , 1610.26, 10.110, 1572.69, -37.57, ,
EAI: $1220 Current yield: 4.71%, , , , , , , , ,
Security total, 2564.399, 10.190, 24522.00, 26132.26, , 25926.07, -206.19, 1404.07,
NAME_3 & NAME_4 PREFERRED, , , , , , , , ,
SEC & INC FUND I, , , , , , , , ,
Symbol: CPXIX, , , , , , , , ,
Trade date: Sep 23 20, 740.474, 13.910, 10300.00, 10300.00, 13.330, 9870.51, -429.49, , LT
Total reinvested, 57.946, 14.199, , 822.81, 13.330, 772.42, -50.39, ,
EAI: $539 Current yield: 5.06%, , , , , , , , ,
Security total, 798.420, 13.931, 10300.00, 11122.81, , 10642.93, -479.88, 342.93, '

Retrieve ONLY the following five pieces of information for each holding from the given holdings data: the company name, its symbol or CUSIP, the quantity or number of shares, the price or NAME_1 price, and the market value without outputting anything. If available, use security totals to retrieve numerical data. Give ONLY an CSV response with the retrieved five properties for each holding:


Prompt:
Holdings Data: 'Holding, Number of shares, NAME_1/ Average price per share ($), Client investment ($), Cost basis ($), Price per share on Feb 28 ($), Value on Feb 28 ($), Unrealized (tax) gain or loss ($), Investment return ($), Holding period
FEDERATED NAME_2, , , , , , , , ,
STRATEGIC VALUE DIVIDEND, , , , , , , , ,
FUND IS, , , , , , , , ,
Symbol: SVAIX, , , , , , , , ,
Trade date: Feb 4 21, 1366.279, 5.160, 7050.00, 7050.00, 6.110, 8347.96, 1297.96, , LT
Total reinvested, 57.410, 5.678, , 326.02, 6.110, 350.77, 24.75, ,
EAI: $313 Current yield: 3.60%
Security total, 1423.689, 5.181, 7050.00, 7376.02, , 8698.73, 1322.71, 1648.73,
O'SHAUGHNESSY MARKET LEADERS VALUE FUND CLASS, , , , , , , , ,
I, , , , , , , , ,
Symbol: OFVIX, , , , , , , , ,
Trade date: Feb 4 21, 470.628, 14.979, 7050.00, 7050.00, 17.710, 8334.81, 1284.81, , LT
Total reinvested, 8.717, 17.859, , 155.68, 17.710, 154.38, -1.30, ,
EAI: $159 Current yield: 1.87%, , , , , , , , ,
Security total, 479.345, 15.032, 7050.00, 7205.68, , 8489.19, 1283.51, 1439.19,
PACE portfolio total, , , $14100.00, $14581.70, , $17187.92, $2606.22, $3087.92,
NAME_3 MULTI-STRATEGY, , , , , , , , ,
INCOME FUND CLASS INSTL, , , , , , , , ,
Symbol: ANGIX, , , , , , , , ,
Trade date: Sep 23 20, 2408.841, 10.179, 24522.00, 24522.00, 10.110, 24353.38, -168.62, , LT
Total reinvested, 155.558, 10.351, , 1610.26, 10.110, 1572.69, -37.57, ,
EAI: $1220 Current yield: 4.71%, , , , , , , , ,
Security total, 2564.399, 10.190, 24522.00, 26132.26, , 25926.07, -206.19, 1404.07,
NAME_4 & NAME_5 PREFERRED, , , , , , , , ,
SEC & INC FUND I, , , , , , , , ,
Symbol: CPXIX, , , , , , , , ,
Trade date: Sep 23 20, 740.474, 13.910, 10300.00, 10300.00, 13.330, 9870.51, -429.49, , LT
Total reinvested, 57.946, 14.199, , 822.81, 13.330, 772.42, -50.39, ,
EAI: $539 Current yield: 5.06%, , , , , , , , ,
Security total, 798.420, 13.931, 10300.00, 11122.81, , 10642.93, -479.88, 342.93,'

Get ONLY the following five pieces of information for each holding from the given holdings data: the company name, its symbol or CUSIP, the quantity / number of shares, the price / NAME_1 of each share, and the market value / value without outputting anything. Give ONLY an CSV response with the retrieved five properties for each holding where every output row is a unique security.


Prompt:
traduza para o portugues brasil corrigindo as concordancias: 1
00:00:02,580 --> 00:00:21,350
[Music]

2
00:00:22,849 --> 00:00:27,840
given the kids to dream a vision that

3
00:00:27,840 --> 00:00:29,780
your heart is all

4
00:00:29,780 --> 00:00:35,840
[Music]

5
00:00:35,840 --> 00:00:41,629
carry the best bars you can pass the

6
00:00:41,629 --> 00:00:46,399
sister to brother NAME_1

7
00:00:47,480 --> 00:00:50,620
[Music]

8
00:01:08,510 --> 00:01:12,380
world famous athletes astronauts

9
00:01:12,380 --> 00:01:14,670
international personalities who have

10
00:01:14,670 --> 00:01:16,500
really made a difference in our world

11
00:01:16,500 --> 00:01:20,370
you and I we all started out exactly the

12
00:01:20,370 --> 00:01:22,050
same just like that beautiful little

13
00:01:22,050 --> 00:01:25,190
baby that you just saw we were born rich

14
00:01:25,190 --> 00:01:27,660
rich in potential we have deep

15
00:01:27,660 --> 00:01:29,640
reservoirs of talent and ability within

16
00:01:29,640 --> 00:01:32,700
us you're about to embark on one of the

17
00:01:32,700 --> 00:01:34,290
most exciting journeys you could

18
00:01:34,290 --> 00:01:36,930
possibly get involved with this program

19
00:01:36,930 --> 00:01:39,300
you were born wrench is the most

20
00:01:39,300 --> 00:01:41,460
effective personal development program

21
00:01:41,460 --> 00:01:43,800
in the world today its butt isn't

22
00:01:43,800 --> 00:01:46,470
designed specifically to help you

23
00:01:46,470 --> 00:01:49,410
realize all of your dreams live the life

24
00:01:49,410 --> 00:01:52,259
that you really want to live we're going

25
00:01:52,259 --> 00:01:54,570
to invite you in to a live seminar so

26
00:01:54,570 --> 00:01:56,580
that you can experience the excitement

27
00:01:56,580 --> 00:01:59,190
the high energy of actually being there

28
00:01:59,190 --> 00:02:01,950
we have special guest NAME_2 on the

29
00:02:01,950 --> 00:02:03,710
program you'll be meeting other

30
00:02:03,710 --> 00:02:06,149
personalities people just like you and I

31
00:02:06,149 --> 00:02:08,160
who have been involved in this program

32
00:02:08,160 --> 00:02:10,470
and it's made a tremendous difference in

33
00:02:10,470 --> 00:02:15,090
their life consider this just like that

34
00:02:15,090 --> 00:02:17,670
baby when you and I were born we were

35
00:02:17,670 --> 00:02:20,820
natural born risk takers we had no

36
00:02:20,820 --> 00:02:24,060
limitations we were free to step out and

37
00:02:24,060 --> 00:02:26,760
do everythin