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 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 81.87 % 78.14 % 86.39 % 82.00 % 86.92 % 89.08 % 91.58 % 88.04 %

Benchmark TP FP FN
CAR 31744 2648 702

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.24 % 86.82 % 90.26 % 8 78.07 %

Benchmark MT rate PT rate ML rate FRAG
CAR 83.08 % 5.23 % 11.69 % 77

Benchmark # Dets # Tracks
CAR 32446 593

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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