Method

3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association [CasTrack]


Submitted on 24 Aug. 2024 17:42 by
Mohamed Mostafa (Khalifa University)

Running time:1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Re-run of the paper "3D Multi-Object Tracking in
Point Clouds Based on Prediction Confidence-Guided
Data Association" CasTrack using their official
code.
Parameters:
N/A
Latex Bibtex:
@ARTICLE{9352500,
author={Wu, Hai and Han, Wenkai and Wen, Chenglu
and Li, Xin and Wang, Cheng},
journal={IEEE Transactions on Intelligent
Transportation Systems},
title={3D Multi-Object Tracking in Point Clouds
Based on Prediction Confidence-Guided Data
Association},
year={2022},
volume={23},
number={6},
pages={5668-5677},
keywords={Three-dimensional
displays;Tracking;Feature extraction;Detectors;Two
dimensional displays;Predictive
models;Acceleration;3D multi-object tracking;point
clouds;data association;object detection and
tracking},
doi={10.1109/TITS.2021.3055616}}

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 90.63 % 86.29 % 91.02 % 88.83 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 95.24 % 96.61 % 95.92 % 36272 1274 1814 11.45 % 43221 909

Benchmark MT PT ML IDS FRAG
CAR 84.62 % 9.38 % 6.00 % 134 204

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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