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 |
91.72 % |
87.49 % |
91.79 % |
90.03 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
95.23 % |
97.54 % |
96.37 % |
37487 |
946 |
1879 |
8.50 % |
44304 |
771 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
86.31 % |
8.46 % |
5.23 % |
23 |
243 |
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.
