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 |
86.22 % |
85.81 % |
86.63 % |
88.69 % |
PEDESTRIAN |
55.58 % |
75.05 % |
56.02 % |
92.36 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
CAR |
89.00 % |
99.14 % |
93.80 % |
34763 |
302 |
4296 |
2.71 % |
38152 |
1645 |
PEDESTRIAN |
61.79 % |
91.98 % |
73.92 % |
14432 |
1258 |
8923 |
11.31 % |
17345 |
1087 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
CAR |
68.15 % |
26.15 % |
5.69 % |
140 |
394 |
PEDESTRIAN |
31.27 % |
41.92 % |
26.80 % |
102 |
734 |
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.