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 |
92.38 % |
87.05 % |
92.59 % |
89.35 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
95.57 % |
97.91 % |
96.72 % |
37588 |
803 |
1744 |
7.22 % |
44914 |
785 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
89.08 % |
7.38 % |
3.54 % |
74 |
407 |
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.
