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 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 81.00 % 78.58 % 84.22 % 84.10 % 84.86 % 87.55 % 90.47 % 87.49 %

Benchmark TP FP FN
CAR 32859 1533 1227

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.91 % 86.08 % 91.97 % 24 78.61 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.77 % 9.23 % 4.00 % 107

Benchmark # Dets # Tracks
CAR 34086 683

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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