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 |
92.63 % |
86.79 % |
92.66 % |
89.21 % |
PEDESTRIAN |
49.13 % |
64.84 % |
50.21 % |
88.68 % |
Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
CAR |
96.47 % |
96.96 % |
96.71 % |
37153 |
1166 |
1358 |
10.48 % |
44527 |
748 |
PEDESTRIAN |
64.31 % |
82.53 % |
72.28 % |
15032 |
3183 |
8344 |
28.61 % |
20090 |
237 |
Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
CAR |
87.85 % |
8.46 % |
3.69 % |
12 |
41 |
PEDESTRIAN |
30.93 % |
34.71 % |
34.36 % |
249 |
1085 |
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.