2020

MOT20: A benchmark for multi object tracking in crowded scenes

Dendorfer, Patrick, Rezatofighi, Hamid, Milan, Anton et al.

Understand

Standardized benchmarks are crucial for the majority of computer vision applications.

Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research.
The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.
The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance.

The clear 2006 evaluation
R. Stiefelhagen, K. Bernardin, R. Bowers, J. S. Garofolo, D. Mostefa, and P. Soundararajan · 2006
Earlier work this paper cites.
Tracking of multiple, partially occluded humans based on static body part detection
B. Wu and R. Nevatia · 2006
Earlier work this paper cites.
Evaluating multiple object tracking performance: The CLEAR MOT metrics
K. Bernardin and R. Stiefelhagen · 2008
Earlier work this paper cites.
A consistent metric for performance evaluation of multi-object filters
D. Schuhmacher, B.-T. Vo, and B.-N. Vo · 2008
Earlier work this paper cites.
Learning to associate: Hybridboosted multi-target tracker for crowded scene
Y. Li, C. Huang, and R. Nevatia · 2009
Earlier work this paper cites.

The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman · 2012
Cited alongside, same era.
Challenges of ground truth evaluation of multi-target tracking
A. Milan, K. Schindler, and S. Roth · 2013
Cited alongside, same era.
Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning
S.-H. Bae and K.-J. Yoon · 2014
Cited alongside, same era.
Face detection without bells and whistles
M. Mathias, R. Benenson, M. Pedersoli, and L. V. Gool · 2014
Cited alongside, same era.
Continuous energy minimization for multitarget tracking
A. Milan, S. Roth, and K. Schindler · 2014
Cited alongside, same era.
Evaluating multi-object tracking
K. Smith, D. Gatica-Perez, J.-M. Odobez, and S. Ba
Cited in the paper.

Multiple target tracking based on undirected hierarchical relation hypergraph
L. Wen, W. Li, J. Yan, Z. Lei, D. Yi, and S. Z. Li · 2014
Later among the works it cites.
Deep Residual Learning for Image Recognition
Original
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Later among the works it cites.
MOTChallenge 2015: Towards a benchmark for multi-target tracking
Original
L. Leal-Taixé, A. Milan, I. Reid, S. Roth, and K. Schindler · 2015
Later among the works it cites.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Original
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Later among the works it cites.
MOT16: A benchmark for multi-object tracking
Original
A. Milan, L. Leal-Taixé, I. Reid, S. Roth, and K. Schindler · 2016
Later among the works it cites.

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