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 |
PEDESTRIAN |
52.35 % |
71.57 % |
53.24 % |
91.17 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
PEDESTRIAN |
60.87 % |
89.47 % |
72.45 % |
14233 |
1676 |
9150 |
15.07 % |
17003 |
595 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
PEDESTRIAN |
34.36 % |
41.92 % |
23.71 % |
206 |
804 |
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.