Method

3D Multi-Object Tracking Based on Uncertainty-Guided Data Association [UG3DMOT]
https://github.com/hejiawei2023/UG3DMOT

Submitted on 20 Nov. 2022 08:12 by
Jiawei He (Chongqing University)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Under the framework of TBD, a 3D MOT method based on
probabilistic data association.
Parameters:
min_hits = 4
miss_age = 15
dis_threshold = 9
Latex Bibtex:
@article{he20243d,
title={3D multi-object tracking based on
informatic divergence-guided data association},
author={He, Jiawei and Fu, Chunyun and Wang,
Xiyang and Wang, Jianwen},
journal={Signal Processing},
volume={222},
pages={109544},
year={2024},
publisher={Elsevier}
}

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 78.60 % 76.01 % 82.28 % 80.77 % 85.44 % 85.36 % 91.37 % 87.84 %

Benchmark TP FP FN
CAR 31399 2993 1111

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 87.98 % 86.56 % 88.07 % 30 75.71 %

Benchmark MT rate PT rate ML rate FRAG
CAR 79.08 % 15.54 % 5.38 % 360

Benchmark # Dets # Tracks
CAR 32510 691

This table as LaTeX

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.


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