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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 88.10 % 86.58 % 88.11 % 89.58 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 92.15 % 96.95 % 94.49 % 35045 1103 2985 9.92 % 40694 810

Benchmark MT PT ML IDS FRAG
CAR 79.23 % 15.38 % 5.38 % 5 330

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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