Method

HRI-SFMOT[la][gp] [HRI-SFMOT]


Submitted on 14 Jan. 2023 13:42 by
Zhicheng Huang (HIKVISION-HRI)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Two cues for handling data association:1) location
of the closest keypoint on 3D bbox;2)velocity vector
calculated by scene flow
A new multistage matching algorithm for balancing
accuracy and efficiency
Parameters:
None
Latex Bibtex:

Detailed Results

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 83.04 % 79.87 % 87.15 % 85.17 % 85.65 % 89.91 % 91.74 % 87.99 %
PEDESTRIAN 46.82 % 41.26 % 53.40 % 45.69 % 58.80 % 57.45 % 65.12 % 71.29 %

Benchmark TP FP FN
CAR 33032 1360 1168
PEDESTRIAN 14733 8417 3256

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 92.62 % 86.70 % 92.65 % 10 79.85 %
PEDESTRIAN 49.16 % 64.52 % 49.58 % 96 26.58 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.85 % 8.46 % 3.69 % 52
PEDESTRIAN 30.58 % 35.05 % 34.36 % 984

Benchmark # Dets # Tracks
CAR 34200 669
PEDESTRIAN 17989 231

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[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.


eXTReMe Tracker