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 |
91.86 % |
86.45 % |
92.03 % |
88.69 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
95.80 % |
97.04 % |
96.42 % |
36890 |
1125 |
1616 |
10.11 % |
45394 |
784 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
89.08 % |
7.69 % |
3.23 % |
57 |
424 |
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.
