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 |
65.13 % |
74.72 % |
65.53 % |
92.41 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| PEDESTRIAN |
71.57 % |
92.60 % |
80.74 % |
16724 |
1336 |
6644 |
12.01 % |
21478 |
310 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| PEDESTRIAN |
40.21 % |
38.14 % |
21.65 % |
92 |
621 |
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.
