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 |
91.96 % |
87.58 % |
92.02 % |
90.13 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
93.89 % |
99.10 % |
96.43 % |
37009 |
336 |
2408 |
3.02 % |
41668 |
682 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
84.77 % |
7.23 % |
8.00 % |
20 |
44 |
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.
