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Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance features.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
Earlier work this paper cites.
A new approach to linear filtering and prediction problems
R. E. Kalman · 1960
Earlier work this paper cites.
Mot20: A benchmark for multi object tracking in crowded scenes
P. Dendorfer, H. Rezatofighi, A. Milan, J. Shi, D. Cremers, I. Reid, S. Roth, K. Schindler, and L. Leal-Taixé · 2003
Earlier work this paper cites.
A mobile vision system for robust multi-person tracking
Andreas Ess, Bastian Leibe, Konrad Schindler, and Luc Van Gool · 2008
Earlier work this paper cites.
Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol
Rangachar Kasturi, Dmitry Goldgof, Padmanabhan Soundararajan, Vasant Manohar, John Garofolo, Rachel Bowers, Matthew Boonstra, Valentina Korzhova, and Jing Zhang · 2008
Earlier work this paper cites.
Global data association for multi-object tracking using network flows
Li Zhang, Yuan Li, and Ramakant Nevatia · 2008
Earlier work this paper cites.
Pedestrian detection: A benchmark
Piotr Dollár, Christian Wojek, Bernt Schiele, and Pietro Perona · 2009
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2009
Earlier work this paper cites.
Learning to associate: Hybridboosted multi-target tracker for crowded scene
Yuan Li, Chang Huang, and Ram Nevatia · 2009
Earlier work this paper cites.
A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, Chia-Chih Chen, Jong Taek Lee, Saurajit Mukherjee, JK Aggarwal, Hyungtae Lee, Larry Davis, et al · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
Earlier work this paper cites.
Fast r-cnn
Ross Girshick · 2015
Earlier work this paper cites.
MOTChallenge 2015: Towards a benchmark for multi-target tracking
L. Leal-Taixé, A. Milan, I. Reid, S. Roth, and K. Schindler · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Fully-convolutional siamese networks for object tracking
Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr · 2016
Earlier work this paper cites.
Simple online and realtime tracking
Alex Bewley, Zongyuan Ge, Lionel Ott, Fabio Ramos, and Ben Upcroft · 2016
Earlier work this paper cites.
Tracking with multi-level features
Roberto Henschel, Laura Leal-Taixé, Bodo Rosenhahn, and Konrad Schindler · 2016
Earlier work this paper cites.
MOT16: A benchmark for multi-object tracking
A. Milan, L. Leal-Taixé, I. Reid, S. Roth, and K. Schindler · 2016
Earlier work this paper cites.
Performance measures and a data set for multi-target, multi-camera tracking
Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi · 2016
Earlier work this paper cites.
Poi: Multiple object tracking with high performance detection and appearance feature
Fengwei Yu, Wenbo Li, Quanquan Li, Yu Liu, Xiaohua Shi, and Junjie Yan · 2016
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On the stability of video detection and tracking
Hong Zhang and Naiyan Wang · 2016
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High-speed tracking-by-detection without using image information
Erik Bochinski, Volker Eiselein, and Thomas Sikora · 2017
Cited alongside, same era.
Detect to track and track to detect
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Simple online and realtime tracking with a deep association metric
See more, know more: Unsupervised video object segmentation with co-attention siamese networks
Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, and Fatih Porikli · 2019
Later among the works it cites.
Mots: Multi-object tracking and segmentation
Paul Voigtlaender, Michael Krause, Aljosa Osep, Jonathon Luiten, Berin Balachandar Gnana Sekar, Andreas Geiger, and Bastian Leibe · 2019
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Spatial-temporal relation networks for multi-object tracking
Jiarui Xu, Yue Cao, Zheng Zhang, and Han Hu · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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Deformable convnets v2: More deformable, better results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2019
Later among the works it cites.
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Dilated residual networks
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Citypersons: A diverse dataset for pedestrian detection
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Person re-identification in the wild
Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, and Qi Tian · 2017
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Relation networks for object detection
Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, and Yichen Wei · 2018
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Real-time multiple people tracking with deeply learned candidate selection and person re-identification
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Lifted disjoint paths with application in multiple object tracking
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Computer vision for autonomous vehicles: Problems, datasets and state of the art
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Retinatrack: Online single stage joint detection and tracking
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Tubetk: Adopting tubes to track multi-object in a one-step training model
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Raft: Recurrent all-pairs field transforms for optical flow
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Video modeling with correlation networks
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Joint detection and multi-object tracking with graph neural networks
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Towards real-time multi-object tracking
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Disentangled non-local neural networks
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A simple baseline for multi-object tracking
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Multiple object tracking by flowing and fusing
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Tracking objects as points
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