Method

LEGO: Learning and Graph Optimized Modular Tracker for Online 3D Multi-Object Tracking [la] [on] [LEGO]


Submitted on 11 May. 2023 10:40 by
Zhenrong Zhang (XJTLU)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
None
Parameters:
None
Latex Bibtex:
@article{zhang2023lego,
title={LEGO: Learning and Graph-Optimized Modular
Tracker for Online Multi-Object Tracking with Point
Clouds},
author={Zhang, Zhenrong and Liu, Jianan and Xia,
Yuxuan and Huang, Tao and Han, Qing-Long and Liu,
Hongbin},
journal={arXiv preprint arXiv:2308.09908},
year={2023}
}

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 90.80 % 86.75 % 91.30 % 89.45 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 96.06 % 96.36 % 96.21 % 38008 1434 1558 12.89 % 44757 1390

Benchmark MT PT ML IDS FRAG
CAR 87.69 % 10.77 % 1.54 % 173 246

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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