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 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 80.75 % 78.91 % 83.27 % 84.64 % 84.94 % 86.87 % 90.19 % 87.92 %

Benchmark TP FP FN
CAR 32823 1569 1445

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.61 % 86.66 % 91.24 % 214 77.88 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.85 % 10.62 % 1.54 % 109

Benchmark # Dets # Tracks
CAR 34268 980

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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