Method

StrongSORT: Make DeepSORT Great Again [StrongSORT++]
[Anonymous Submission]

Submitted on 12 Aug. 2022 09:42 by
[Anonymous Submission]

Running time:0.14 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
We revisit the classic tracker DeepSORT and
upgrade it from various aspects, i.e., detection,
embedding and association.We also present two
lightweight and plug-and-play algorithms to
further refine the tracking results.The final
tracker StrongSORT++ achieves SOTA reuslts on
MOT17/MOT20/DanceTrack.
Parameters:
thr_conf = 0.6
matching_thres = 0.4
Latex Bibtex:
None

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 77.75 % 77.89 % 78.20 % 81.42 % 86.22 % 82.24 % 86.73 % 86.96 %
PEDESTRIAN 54.48 % 52.01 % 57.31 % 57.53 % 70.06 % 63.85 % 70.09 % 78.30 %

Benchmark TP FP FN
CAR 31996 2396 484
PEDESTRIAN 17393 5757 1617

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.35 % 85.42 % 91.63 % 440 76.79 %
PEDESTRIAN 67.38 % 74.24 % 68.15 % 178 48.02 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.31 % 14.62 % 3.08 % 165
PEDESTRIAN 45.36 % 32.30 % 22.34 % 445

Benchmark # Dets # Tracks
CAR 32480 783
PEDESTRIAN 19010 273

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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