Method

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

Submitted on 9 Oct. 2024 15:42 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 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 80.78 % 77.99 % 84.30 % 83.11 % 85.43 % 87.04 % 91.31 % 87.98 %

Benchmark TP FP FN
CAR 32207 2185 1252

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.82 % 86.71 % 90.01 % 64 77.38 %

Benchmark MT rate PT rate ML rate FRAG
CAR 85.85 % 12.77 % 1.38 % 438

Benchmark # Dets # Tracks
CAR 33459 879

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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