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 |
80.83 % |
78.73 % |
80.87 % |
83.71 % |
| PEDESTRIAN |
58.48 % |
71.14 % |
60.47 % |
90.74 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
90.87 % |
91.71 % |
91.29 % |
34470 |
3116 |
3462 |
28.01 % |
42685 |
1651 |
| PEDESTRIAN |
77.76 % |
82.32 % |
79.97 % |
18271 |
3925 |
5226 |
35.28 % |
27439 |
1002 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
73.85 % |
22.92 % |
3.23 % |
16 |
330 |
| PEDESTRIAN |
53.26 % |
36.77 % |
9.97 % |
460 |
1323 |
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.
