From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2].
The tables below show all of these metrics.
| Benchmark |
MOTA |
MOTP |
MODA |
MODP |
| CAR |
90.37 % |
87.01 % |
90.44 % |
89.63 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
93.15 % |
98.36 % |
95.68 % |
36443 |
607 |
2680 |
5.46 % |
40752 |
668 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
81.69 % |
10.00 % |
8.31 % |
24 |
372 |
This table as LaTeX
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[1] K. Bernardin, R. Stiefelhagen:
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia:
Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.
