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This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks.
An algorithm for tracking multiple targets
D.B. Reid · 1979
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An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking
I. J. Cox and S. L. Hingorani · 1996
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Evaluating multiple object tracking performance: the clear mot metrics
Keni Bernardin and Rainer Stiefelhagen · 2008
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Learning to associate: Hybridboosted multi-target tracker for crowded scene
Yuan Li, Chang Huang, and Ram Nevatia · 2009
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis
Sayanan Sivaraman and Mohan Manubhai Trivedi · 2013
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Near-online multi-target tracking with aggregated local flow descriptor
Wongun Choi · 2015
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Joint probabilistic data association revisited
Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, and Ian Reid · 2015
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Multiple hypothesis tracking revisited
Chanho Kim, Fuxin Li, Arridhana Ciptadi, and James M Rehg · 2015
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An introduction to convolutional neural networks
K. O’Shea and R. Nash · 2015
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Looking at humans in the age of self-driving and highly automated vehicles
Eshed Ohn-Bar and Mohan Manubhai Trivedi · 2016
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Cell tracking using deep neural networks with multi-task learning
Tao He, Hua Mao, Jixiang Guo, and Zhang Yi · 2017
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Accurate single stage detector using recurrent rolling convolution
Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Jiahao Pang, Qiong Yan, Yu-Wing Tai, and Li Xu · 2017
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Crowd behavior analysis: A survey
HY Swathi, G Shivakumar, and HS Mohana · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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How would surround vehicles move? a unified framework for maneuver classification and motion prediction
Nachiket Deo, Akshay Rangesh, and Mohan M Trivedi · 2018
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A literature review on the prediction of pedestrian behavior in urban scenarios
Daniela Ridel, Eike Rehder, Martin Lauer, Christoph Stiller, and Denis Wolf · 2018
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Robust multi-modality multi-object tracking
Wenwei Zhang, Hui Zhou, Shuyang Sun, Zhe Wang, Jianping Shi, and Chen Change Loy · 2019
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Learning a neural solver for multiple object tracking
Guillem Brasó and Laura Leal-Taixé · 2020
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Trajectory forecasts in unknown environments conditioned on grid-based plans
Nachiket Deo and Mohan M Trivedi · 2020
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Smat: Smart multiple affinity metrics for multiple object tracking
Nicolas Franco Gonzalez, Andres Ospina, and Philippe Calvez · 2020
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Graph networks for multiple object tracking
Jiahe Li, Xu Gao, and Tingting Jiang · 2020
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Beyond pixels: Leveraging geometry and shape cues for online multi-object tracking
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Joint monocular 3d vehicle detection and tracking
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Mots: Multi-object tracking and segmentation
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Johannes Pöschmann, Tim Pfeifer, and Peter Protzel · 2020
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SpAGNN spatially-aware graph neural networks for relational behavior forecasting from sensor data
Renjie Liao Sergio Casas, Cole Gulino and Raquel Urtasun · 2020
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Jrmot: A real-time 3d multi-object tracker and a new large-scale dataset
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Joint 3d tracking and forecasting with graph neural network and diversity sampling
X. Weng, Y. Yuan, and K. Kitani · 2020
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Joint object detection and multi-object tracking with graph neural networks
X. Weng Y. Wang, K. Kitani · 2020
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Fairmot: On the fairness of detection and re-identification in multiple object tracking
Yifu Zhang, Chunyu Wang, Xinggang Wang, Wenjun Zeng, and Wenyu Liu · 2020
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Tracking objects as points
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
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