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 |
65.14 % |
64.64 % |
66.38 % |
72.51 % |
73.25 % |
69.65 % |
83.33 % |
81.50 % |
| PEDESTRIAN |
51.26 % |
49.71 % |
53.25 % |
58.58 % |
62.11 % |
58.65 % |
70.14 % |
75.79 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
30925 |
3467 |
3121 |
| PEDESTRIAN |
17840 |
5310 |
3993 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
80.22 % |
78.95 % |
80.84 % |
213 |
61.30 % |
| PEDESTRIAN |
58.40 % |
71.18 % |
59.81 % |
328 |
36.19 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
73.85 % |
22.92 % |
3.23 % |
324 |
| PEDESTRIAN |
52.92 % |
36.77 % |
10.31 % |
1078 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
34046 |
1280 |
| PEDESTRIAN |
21833 |
737 |
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.
