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 |
92.55 % |
86.73 % |
92.59 % |
89.42 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
96.60 % |
96.85 % |
96.73 % |
37638 |
1224 |
1324 |
11.00 % |
44402 |
774 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
88.15 % |
7.69 % |
4.15 % |
14 |
55 |
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.
