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 |
64.00 % |
76.45 % |
64.94 % |
82.85 % |
| PEDESTRIAN |
21.37 % |
70.23 % |
23.94 % |
92.72 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
74.14 % |
92.49 % |
82.30 % |
28038 |
2278 |
9779 |
20.48 % |
34107 |
774 |
| PEDESTRIAN |
40.24 % |
71.70 % |
51.55 % |
9367 |
3698 |
13910 |
33.24 % |
15372 |
223 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
41.69 % |
38.15 % |
20.15 % |
325 |
783 |
| PEDESTRIAN |
14.09 % |
31.62 % |
54.30 % |
594 |
1161 |
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.
