Method

3D Multi-Level Associations [3DMLA]


Submitted on 15 Jun. 2023 09:33 by
Minho Cho (Yonsei Univ.)

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{cho20233d,
title={3D LiDAR Multi-Object Tracking with
Short-Term and Long-Term Multi-Level
Associations},
author={Cho, Minho and Kim, Euntai},
journal={Remote Sensing},
volume={15},
number={23},
pages={5486},
year={2023},
publisher={MDPI}
}

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 85.12 % 84.91 % 85.17 % 87.92 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.24 % 96.40 % 93.22 % 35070 1308 3793 11.76 % 41708 926

Benchmark MT PT ML IDS FRAG
CAR 70.62 % 23.54 % 5.85 % 15 318

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.


eXTReMe Tracker