CSTrack: Rethinking the competition between detection and ReID in Multi-Object Tracking


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Benchmark:

Short name:

CSTrack

Detector:

Private

Description:

CSTrack proposes a strong ReID based one-shot MOT framework. It includes a novel cross-correlation network that can effectively impel the separate branches to learn task-dependent representations, and a scale-aware attention network that learns discriminative embeddings to improve the ReID capability. This work also provides an analysis of the weak data association ability in one-shot MOT methods. Our improvements make the data association ability of our one-shot model is comparable to two-stage methods while running more faster.

Reference:

C. Liang, Z. Zhang, Y. Lu, X. Zhou, B. Li. Rethinking the competition between detection and ReID in Multi-Object Tracking. In arXiv:2010.12138 [cs], 2020.

Last submitted:

December 23, 2020 (3 years ago)

Published:

December 15, 2020 at 07:25:46 CET

Submissions:

2

Project page / code:

Open source:

Yes

Hardware:

RTX2080ti

Runtime:

4.5 Hz

Benchmark performance:

Sequence MOTA IDF1 HOTA MT ML FP FN Rcll Prcn AssA DetA AssRe AssPr DetRe DetPr LocA FAF ID Sw. Frag
MOT2066.668.654.0626 (50.4)192 (15.5)25,404144,35872.193.654.054.257.677.858.576.081.55.73,196 (44.3)7,632 (105.9)

Detailed performance:

Sequence MOTA IDF1 HOTA MT ML FP FN Rcll Prcn AssA DetA AssRe AssPr DetRe DetPr LocA FAF ID Sw. Frag
MOT20-0480.279.061.24393212,89940,22685.394.859.363.462.979.068.676.281.26.21,0213,061
MOT20-0650.753.943.780866,23458,18556.292.344.143.547.375.146.576.582.46.21,0652,547
MOT20-0773.566.455.07332,4755,91082.191.750.061.354.276.167.975.882.44.2398523
MOT20-0842.550.739.334713,79640,03748.390.842.636.445.675.439.073.481.34.77121,501

Raw data: