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 |
92.76 % |
85.73 % |
92.85 % |
87.75 % |
| PEDESTRIAN |
72.29 % |
73.28 % |
73.59 % |
91.13 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
97.24 % |
96.36 % |
96.80 % |
37150 |
1403 |
1055 |
12.61 % |
47288 |
716 |
| PEDESTRIAN |
79.86 % |
93.12 % |
85.99 % |
18756 |
1385 |
4729 |
12.45 % |
24291 |
400 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
90.31 % |
4.31 % |
5.38 % |
31 |
72 |
| PEDESTRIAN |
52.58 % |
37.11 % |
10.31 % |
301 |
926 |
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.
