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 |
78.78 % |
79.86 % |
78.93 % |
84.39 % |
| PEDESTRIAN |
50.84 % |
71.87 % |
51.08 % |
91.86 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
81.87 % |
98.28 % |
89.33 % |
30337 |
531 |
6717 |
4.77 % |
33617 |
784 |
| PEDESTRIAN |
55.89 % |
92.72 % |
69.74 % |
13051 |
1024 |
10302 |
9.21 % |
15160 |
286 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
52.15 % |
36.46 % |
11.38 % |
49 |
324 |
| PEDESTRIAN |
26.12 % |
39.86 % |
34.02 % |
54 |
589 |
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.
