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 |
92.02 % |
85.78 % |
92.41 % |
88.46 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
CAR |
94.23 % |
99.01 % |
96.56 % |
36668 |
366 |
2244 |
3.29 % |
45993 |
1629 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
CAR |
86.77 % |
10.92 % |
2.31 % |
134 |
330 |
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.