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 |
79.13 % |
78.81 % |
80.13 % |
82.41 % |
86.43 % |
83.40 % |
88.81 % |
87.11 % |
| PEDESTRIAN |
52.72 % |
53.55 % |
52.21 % |
58.87 % |
70.15 % |
59.50 % |
65.00 % |
77.69 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
32256 |
2136 |
537 |
| PEDESTRIAN |
17759 |
5391 |
1670 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
91.72 % |
85.74 % |
92.23 % |
173 |
78.35 % |
| PEDESTRIAN |
68.37 % |
73.60 % |
69.50 % |
262 |
48.12 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
85.85 % |
11.54 % |
2.62 % |
172 |
| PEDESTRIAN |
51.55 % |
34.36 % |
14.09 % |
741 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
32793 |
731 |
| PEDESTRIAN |
19429 |
336 |
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.
