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 |
91.06 % |
86.86 % |
91.16 % |
89.56 % |
| PEDESTRIAN |
39.57 % |
64.66 % |
40.40 % |
91.23 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
93.62 % |
98.60 % |
96.04 % |
36912 |
525 |
2516 |
4.72 % |
42233 |
680 |
| PEDESTRIAN |
48.09 % |
86.71 % |
61.87 % |
11193 |
1716 |
12081 |
15.43 % |
13540 |
274 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
85.38 % |
6.31 % |
8.31 % |
32 |
97 |
| PEDESTRIAN |
21.99 % |
29.21 % |
48.80 % |
192 |
882 |
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.
