Method

CasTrack[la] [CasTrack]
https://github.com/hailanyi/3D-Multi-Object-Tracker

Submitted on 23 Sep. 2022 13:35 by
hai wu (xiamen university)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
A tracker based on CasA detections.
Parameters:
None
Latex Bibtex:
@article{casa2022,
title={CasA: A Cascade Attention Network for
3D Object Detection from LiDAR point clouds},
author={Wu, Hai and Deng, Jinhao and Wen,
Chenglu and Li, Xin and Wang, Cheng},
journal={IEEE TGRS},
year={2022}
}
@article{wu20213d,
title={3D Multi-Object Tracking in Point Clouds
Based on Prediction Confidence-Guided Data
Association},
author={Wu, Hai and Han, Wenkai and Wen, Chenglu
and Li, Xin and Wang, Cheng},
journal={IEEE TITS},
year={2021}
}

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 91.93 % 86.19 % 91.99 % 88.54 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 95.93 % 96.72 % 96.33 % 36126 1225 1531 11.01 % 43475 784

Benchmark MT PT ML IDS FRAG
CAR 86.77 % 9.23 % 4.00 % 21 107

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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