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 |
82.08 % |
78.80 % |
86.10 % |
82.33 % |
87.39 % |
89.13 % |
91.19 % |
88.02 % |
| PEDESTRIAN |
39.50 % |
32.30 % |
48.52 % |
34.03 % |
61.62 % |
52.68 % |
63.03 % |
71.01 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
31865 |
2527 |
534 |
| PEDESTRIAN |
11013 |
12137 |
1772 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
91.05 % |
86.79 % |
91.10 % |
17 |
78.81 % |
| PEDESTRIAN |
39.45 % |
64.44 % |
39.92 % |
108 |
22.54 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
85.39 % |
6.31 % |
8.31 % |
94 |
| PEDESTRIAN |
21.99 % |
29.21 % |
48.80 % |
840 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
32399 |
637 |
| PEDESTRIAN |
12785 |
259 |
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.
