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 |
86.01 % |
85.94 % |
86.48 % |
88.74 % |
PEDESTRIAN |
52.47 % |
74.69 % |
52.89 % |
92.43 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
CAR |
89.06 % |
98.96 % |
93.75 % |
34870 |
368 |
4282 |
3.31 % |
38643 |
1628 |
PEDESTRIAN |
62.06 % |
87.70 % |
72.68 % |
14506 |
2035 |
8870 |
18.29 % |
19038 |
1381 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
CAR |
69.23 % |
25.38 % |
5.38 % |
160 |
421 |
PEDESTRIAN |
31.27 % |
43.30 % |
25.43 % |
98 |
741 |
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.