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 |
77.12 % |
73.43 % |
81.66 % |
80.60 % |
81.69 % |
84.23 % |
90.45 % |
86.79 % |
| PEDESTRIAN |
44.21 % |
39.39 % |
50.12 % |
44.81 % |
56.35 % |
54.63 % |
64.47 % |
71.15 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
31787 |
2605 |
2147 |
| PEDESTRIAN |
14667 |
8483 |
3739 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
85.82 % |
85.22 % |
86.18 % |
125 |
72.16 % |
| PEDESTRIAN |
46.34 % |
64.44 % |
47.20 % |
201 |
23.81 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
82.61 % |
15.38 % |
2.00 % |
604 |
| PEDESTRIAN |
31.27 % |
49.14 % |
19.59 % |
1530 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
33934 |
1148 |
| PEDESTRIAN |
18406 |
983 |
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.
