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 |
92.09 % |
85.78 % |
92.49 % |
88.46 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
CAR |
94.30 % |
99.01 % |
96.60 % |
36693 |
366 |
2218 |
3.29 % |
46019 |
1621 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
CAR |
86.62 % |
11.08 % |
2.31 % |
136 |
334 |
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.