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 |
65.86 % |
62.89 % |
69.88 % |
65.84 % |
80.28 % |
73.23 % |
84.52 % |
82.52 % |
| PEDESTRIAN |
44.34 % |
38.52 % |
51.31 % |
41.14 % |
68.86 % |
55.02 % |
73.05 % |
76.47 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
27652 |
6740 |
554 |
| PEDESTRIAN |
12845 |
10305 |
985 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
78.48 % |
80.11 % |
78.79 % |
107 |
62.49 % |
| PEDESTRIAN |
50.73 % |
71.65 % |
51.23 % |
116 |
35.00 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
51.69 % |
36.46 % |
11.85 % |
280 |
| PEDESTRIAN |
26.46 % |
39.17 % |
34.36 % |
500 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
28206 |
676 |
| PEDESTRIAN |
13830 |
262 |
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.
