Method

FastTrack [FastTrack]


Submitted on 9 Jun. 2022 15:19 by
Chongwei Liu (Dalian University of Technology)

Running time:0.03 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
FastTrack is the first fully GPU-accelerated real-
time MOT system. It only requires boxes with
scores and class ids to do only once association
during a processing, thus allowing for high
generality and efficiency.
Parameters:
detection confidence threshold = 0.4,
IoU matching threshold = 0.005
Latex Bibtex:

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 78.78 % 77.67 % 80.66 % 81.76 % 84.57 % 84.02 % 87.58 % 86.01 %
PEDESTRIAN 55.10 % 52.72 % 57.88 % 58.39 % 69.99 % 63.01 % 71.77 % 78.22 %

Benchmark TP FP FN
CAR 32588 1804 661
PEDESTRIAN 17672 5478 1643

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 92.06 % 84.29 % 92.83 % 264 77.18 %
PEDESTRIAN 67.92 % 74.17 % 69.24 % 305 48.20 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.08 % 10.31 % 2.62 % 104
PEDESTRIAN 46.05 % 39.17 % 14.78 % 487

Benchmark # Dets # Tracks
CAR 33249 766
PEDESTRIAN 19315 367

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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