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Multi-object tracking (MOT) is a challenging vision task that aims to detect individual objects within a single frame and associate them across multiple frames.
Graph neural based end-to-end data association framework for online multiple-object tracking
Jiang, X.; Li, P.; Li, Y.; and Zhen, X. 2019 · 1907
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The Hungarian method for the assignment problem
Kuhn, H. W. 1955 · 1955
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An introduction to the Kalman filter
Welch, G.; Bishop, G.; et al. 1995 · 1995
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Mot20: A benchmark for multi object tracking in crowded scenes
Dendorfer, P.; Rezatofighi, H.; Milan, A.; Shi, J.; Cremers, D.; Reid, I.; Roth, S.; Schindler, K.; and Leal-Taixé, L. 2020 · 2003
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Yolov4: Optimal speed and accuracy of object detection
Bochkovskiy, A.; Wang, C.-Y.; and Liao, H.-Y. M. 2020 · 2004
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A new model for learning in graph domains
Gori, M.; Monfardini, G.; and Scarselli, F. 2005 · 2005
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Evaluating multiple object tracking performance: the clear mot metrics
Bernardin, K.; and Stiefelhagen, R. 2008 · 2008
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Global data association for multi-object tracking using network flows
Zhang, L.; Li, Y.; and Nevatia, R. 2008 · 2008
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Deformable detr: Deformable transformers for end-to-end object detection
Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2020 · 2010
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Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2020 · 2011
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Transtrack: Multiple object tracking with transformer
Sun, P.; Cao, J.; Jiang, Y.; Zhang, R.; Xie, E.; Yuan, Z.; Wang, C.; and Luo, P. 2020 · 2012
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Simple online and realtime tracking
Bewley, A.; Ge, Z.; Ott, L.; Ramos, F.; and Upcroft, B. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
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MOT16: A benchmark for multi-object tracking
Milan, A.; Leal-Taixé, L.; Reid, I.; Roth, S.; and Schindler, K. 2016 · 2016
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Performance measures and a data set for multi-target, multi-camera tracking
Ristani, E.; Solera, F.; Zou, R.; Cucchiara, R.; and Tomasi, C. 2016 · 2016
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Focal Loss for Dense Object Detection
Lin, T.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
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Feature pyramid networks for object detection
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; and Belongie, S. 2017 · 2017
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Simple online and realtime tracking with a deep association metric
Wojke, N.; Bewley, A.; and Paulus, D. 2017 · 2017
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mixup: Beyond empirical risk minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2017 · 2017
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Real-time multiple people tracking with deeply learned candidate selection and person re-identification
Chen, L.; Ai, H.; Zhuang, Z.; and Shang, C. 2018 · 2018
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Acquisition of localization confidence for accurate object detection
Jiang, B.; Luo, R.; Mao, J.; Xiao, T.; and Jiang, Y. 2018 · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2018 · 2018
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Centernet: Keypoint triplets for object detection
Trackmpnn: A message passing graph neural architecture for multi-object tracking
Rangesh, A.; Maheshwari, P.; Gebre, M.; Mhatre, S.; Ramezani, V.; and Trivedi, M. M. 2021 · 2021
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Learning to track with object permanence
Tokmakov, P.; Li, J.; Burgard, W.; and Gaidon, A. 2021 · 2021
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Fairmot: On the fairness of detection and re-identification in multiple object tracking
Zhang, Y.; Wang, C.; Wang, X.; Zeng, W.; and Liu, W. 2021 · 2021
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BoT-SORT: Robust associations multi-pedestrian tracking
Aharon, N.; Orfaig, R.; and Bobrovsky, B.-Z. 2022 · 2022
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MeMOT: multi-object tracking with memory
Cai, J.; Xu, M.; Li, W.; Xiong, Y.; Xia, W.; Tu, Z.; and Soatto, S. 2022 · 2022
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Duan, K.; Bai, S.; Xie, L.; Qi, H.; Huang, Q.; and Tian, Q. 2019 · 2019
Cited alongside, same era.
Generalized intersection over union: A metric and a loss for bounding box regression
Rezatofighi, H.; Tsoi, N.; Gwak, J.; Sadeghian, A.; Reid, I.; and Savarese, S. 2019 · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y.; and Ermon, S. 2019 · 2019
Cited alongside, same era.
Learning a neural solver for multiple object tracking
Brasó, G.; and Leal-Taixé, L. 2020 · 2020
Cited alongside, same era.
End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Graph networks for multiple object tracking
Li, J.; Gao, X.; and Jiang, T. 2020 · 2020
Cited alongside, same era.
Cao, J.; Weng, X.; Khirodkar, R.; Pang, J.; and Kitani, K. 2022 · 2022
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Diffusiondet: Diffusion model for object detection
Chen, S.; Sun, P.; Song, Y.; and Luo, P. 2022 · 2022
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Strongsort: Make deepsort great again
Du, Y.; Song, Y.; Yang, B.; and Zhao, Y. 2022 · 2022
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DiffusionInst: Diffusion Model for Instance Segmentation
Gu, Z.; Chen, H.; Xu, Z.; Lan, J.; Meng, C.; and Wang, W. 2022 · 2022
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Trackformer: Multi-object tracking with transformers
Meinhardt, T.; Kirillov, A.; Leal-Taixe, L.; and Feichtenhofer, C. 2022 · 2022
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Dancetrack: Multi-object tracking in uniform appearance and diverse motion
Sun, P.; Cao, J.; Jiang, Y.; Yuan, Z.; Bai, S.; Kitani, K.; and Luo, P. 2022 · 2022
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TransCenter: Transformers with dense representations for multiple-object tracking
Xu, Y.; Ban, Y.; Delorme, G.; Gan, C.; Rus, D.; and Alameda-Pineda, X. 2022 · 2022
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Motr: End-to-end multiple-object tracking with transformer
Zeng, F.; Dong, B.; Zhang, Y.; Wang, T.; Zhang, X.; and Wei, Y. 2022 · 2022
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Bytetrack: Multi-object tracking by associating every detection box
Zhang, Y.; Sun, P.; Jiang, Y.; Yu, D.; Weng, F.; Yuan, Z.; Luo, P.; Liu, W.; and Wang, X. 2022 · 2022
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Tracking objects as pixel-wise distributions
Zhao, Z.; Wu, Z.; Zhuang, Y.; Li, B.; and Jia, J. 2022 · 2022
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Global Tracking Transformers
Zhou, X.; Yin, T.; Koltun, V.; and Krähenbühl, P. 2022 · 2022
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SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
Liu, Z.; Wang, X.; Wang, C.; Liu, W.; and Bai, X. 2023 · 2023
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