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 |
90.98 % |
86.55 % |
91.04 % |
89.30 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
95.69 % |
96.49 % |
96.09 % |
37865 |
1376 |
1707 |
12.37 % |
44542 |
662 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
83.85 % |
6.15 % |
10.00 % |
18 |
66 |
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.
