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 |
91.17 % |
86.57 % |
91.23 % |
89.33 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
95.68 % |
96.66 % |
96.17 % |
37858 |
1307 |
1710 |
11.75 % |
44448 |
660 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
83.54 % |
6.31 % |
10.15 % |
19 |
64 |
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.
