Method

MCTrack_online [MCTrack_online]
https://github.com/megvii-research/MCTrack

Submitted on 15 Nov. 2024 08:47 by
Xiyang Wang (Mach-drive)

Running time:0.01 s
Environment:>8 cores @ 3.5 Ghz (Python)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{wang2024mctrack,
title={MCTrack: A Unified 3D Multi-Object Tracking
Framework for Autonomous Driving},
author={Wang, Xiyang and Qi, Shouzheng and Zhao,
Jieyou and Zhou, Hangning and Zhang, Siyu and Wang,
Guoan and Tu, Kai and Guo, Songlin and Zhao, Jianbo
and Li, Jian and others},
journal={arXiv preprint arXiv:2409.16149},
year={2024}
}

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 89.86 % 86.94 % 90.00 % 89.55 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.90 % 96.34 % 95.61 % 37458 1425 2014 12.81 % 44108 1165

Benchmark MT PT ML IDS FRAG
CAR 87.69 % 11.08 % 1.23 % 50 373

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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