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 |
| PEDESTRIAN |
61.80 % |
73.21 % |
63.94 % |
92.09 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| PEDESTRIAN |
75.05 % |
87.61 % |
80.84 % |
17616 |
2492 |
5857 |
22.40 % |
22937 |
368 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| PEDESTRIAN |
48.45 % |
30.58 % |
20.96 % |
494 |
1027 |
This table as LaTeX
|
[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.
