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 |
67.38 % |
83.98 % |
67.42 % |
87.66 % |
| PEDESTRIAN |
17.71 % |
65.22 % |
18.19 % |
92.02 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
72.65 % |
96.05 % |
82.73 % |
26837 |
1104 |
10101 |
9.92 % |
28923 |
851 |
| PEDESTRIAN |
31.87 % |
71.09 % |
44.01 % |
7445 |
3027 |
15913 |
27.21 % |
10863 |
401 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
43.08 % |
36.77 % |
20.15 % |
13 |
212 |
| PEDESTRIAN |
9.97 % |
23.02 % |
67.01 % |
110 |
674 |
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.
