From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics.
The tables below show all of these metrics.
| Benchmark |
HOTA |
DetA |
AssA |
DetRe |
DetPr |
AssRe |
AssPr |
LocA |
| CAR |
79.53 % |
77.68 % |
82.13 % |
81.43 % |
85.89 % |
85.05 % |
89.81 % |
86.95 % |
| PEDESTRIAN |
50.94 % |
49.33 % |
52.85 % |
54.13 % |
71.13 % |
57.36 % |
73.97 % |
78.97 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
32040 |
2352 |
564 |
| PEDESTRIAN |
16097 |
7053 |
1520 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
90.65 % |
85.50 % |
91.52 % |
300 |
77.14 % |
| PEDESTRIAN |
60.69 % |
75.23 % |
62.97 % |
527 |
43.47 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
86.46 % |
11.08 % |
2.46 % |
242 |
| PEDESTRIAN |
43.30 % |
39.86 % |
16.84 % |
759 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
32604 |
1009 |
| PEDESTRIAN |
17617 |
772 |
This table as LaTeX
|
[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe:
HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen:
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia:
Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.
