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 |
69.88 % |
75.07 % |
70.70 % |
91.85 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
PEDESTRIAN |
75.15 % |
94.65 % |
83.78 % |
17516 |
991 |
5791 |
8.91 % |
22187 |
1108 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
PEDESTRIAN |
45.02 % |
46.74 % |
8.25 % |
191 |
1070 |
This table as LaTeX
|
[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.