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 |
90.76 % |
85.83 % |
91.09 % |
88.46 % |
| PEDESTRIAN |
63.48 % |
74.92 % |
64.13 % |
92.17 % |
| Benchmark |
recall |
precision |
F1 |
TP |
FP |
FN |
FAR |
#objects |
#trajectories |
| CAR |
93.08 % |
99.02 % |
95.96 % |
36376 |
360 |
2705 |
3.24 % |
44742 |
1267 |
| PEDESTRIAN |
71.15 % |
91.39 % |
80.01 % |
16619 |
1565 |
6740 |
14.07 % |
21337 |
562 |
| Benchmark |
MT |
PT |
ML |
IDS |
FRAG |
| CAR |
83.08 % |
14.00 % |
2.92 % |
112 |
363 |
| PEDESTRIAN |
42.61 % |
40.21 % |
17.18 % |
149 |
841 |
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.
