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 |
86.48 % |
86.77 % |
86.89 % |
89.60 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
90.97 % |
96.92 % |
93.85 % |
34412 |
1093 |
3417 |
9.83 % |
39784 |
1334 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
76.46 % |
20.62 % |
2.92 % |
139 |
692 |
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.
