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 |
90.91 % |
85.63 % |
91.66 % |
88.27 % |
| PEDESTRIAN |
60.90 % |
75.37 % |
62.19 % |
92.24 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
94.02 % |
98.55 % |
96.23 % |
36596 |
540 |
2328 |
4.85 % |
46627 |
1732 |
| PEDESTRIAN |
69.96 % |
90.46 % |
78.90 % |
16364 |
1725 |
7027 |
15.51 % |
20801 |
980 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
86.92 % |
10.62 % |
2.46 % |
259 |
454 |
| PEDESTRIAN |
43.99 % |
39.52 % |
16.49 % |
299 |
904 |
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.
