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 |
90.88 % |
85.57 % |
91.43 % |
88.41 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
CAR |
93.83 % |
98.50 % |
96.11 % |
36428 |
555 |
2394 |
4.99 % |
46229 |
1431 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
CAR |
86.92 % |
11.08 % |
2.00 % |
189 |
426 |
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.