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
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[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.