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 |
89.90 % |
87.02 % |
90.61 % |
89.73 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
92.78 % |
98.94 % |
95.76 % |
36484 |
392 |
2838 |
3.52 % |
40684 |
1039 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
81.08 % |
9.69 % |
9.23 % |
244 |
271 |
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.
