Method

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

Submitted on 9 Nov. 2022 03:17 by
hai wu (xiamen university)

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

Method Description:
A tracker based on VirConv-T detections.
Parameters:
TBD
Latex Bibtex:
@inproceedings{VirConv,
title={Virtual Sparse Convolution for Multimodal
3D Object Detection},
author={Wu, Hai and Wen,Chenglu and Shi,
Shaoshuai and Wang, Cheng},
booktitle={CVPR},
year={2023}
}

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 90.28 % 86.93 % 90.32 % 89.83 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.18 % 98.12 % 95.58 % 36042 692 2638 6.22 % 41011 655

Benchmark MT PT ML IDS FRAG
CAR 83.23 % 5.08 % 11.69 % 12 66

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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