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 |
63.88 % |
70.28 % |
65.02 % |
90.90 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
PEDESTRIAN |
72.22 % |
91.44 % |
80.70 % |
16935 |
1585 |
6513 |
14.25 % |
20960 |
625 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
PEDESTRIAN |
46.39 % |
31.62 % |
21.99 % |
264 |
1066 |
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.