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 |
78.84 % |
77.50 % |
80.77 % |
81.05 % |
86.26 % |
83.45 % |
89.87 % |
87.04 % |
| PEDESTRIAN |
53.33 % |
51.44 % |
55.56 % |
56.27 % |
71.21 % |
60.20 % |
73.25 % |
78.58 % |
| Benchmark |
TP |
FP |
FN |
| CAR |
31875 |
2517 |
440 |
| PEDESTRIAN |
16858 |
6292 |
1435 |
| Benchmark |
MOTA |
MOTP |
MODA |
IDSW |
sMOTA |
| CAR |
90.77 % |
85.61 % |
91.40 % |
216 |
77.44 % |
| PEDESTRIAN |
65.34 % |
74.54 % |
66.62 % |
296 |
46.81 % |
| Benchmark |
MT rate |
PT rate |
ML rate |
FRAG |
| CAR |
84.15 % |
12.92 % |
2.92 % |
269 |
| PEDESTRIAN |
44.67 % |
38.83 % |
16.50 % |
664 |
| Benchmark |
# Dets |
# Tracks |
| CAR |
32315 |
888 |
| PEDESTRIAN |
18293 |
491 |
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.
