Method

BiTrack [BiTrack]


Submitted on 22 Sep. 2024 15:04 by
Kemiao Huang (Southern University of Science and Technology)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Bidirectional MOT optimization with 2D-3D fusion.
Parameters:
Please check the paper.
Latex Bibtex:
@misc{huang2024bitrackbidirectionaloffline3d,
title={BiTrack: Bidirectional Offline 3D
Multi-Object Tracking Using Camera-LiDAR Data},
author={Kemiao Huang and Meiying Zhang and
YinQi Chen and
Qi Hao},
year={2024},
eprint={2406.18414},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2406.18414},
}

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 91.63 % 87.48 % 91.66 % 90.10 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 95.02 % 97.62 % 96.31 % 37376 910 1958 8.18 % 43817 723

Benchmark MT PT ML IDS FRAG
CAR 85.85 % 7.08 % 7.08 % 12 233

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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