Method

Virtual Sparse Convolution for Multimodal 3D Object Detection [VirConvTrack]


Submitted on 24 Aug. 2024 15:35 by
Mohamed Mostafa (Khalifa University)

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

Method Description:
This is a re-run of the paper "Virtual Sparse
Convolution for Multimodal 3D Object Detection"
from their public code.
Parameters:
N/A
Latex Bibtex:
@INPROCEEDINGS{10205191,
author={Wu, Hai and Wen, Chenglu and Shi,
Shaoshuai and Li, Xin and Wang, Cheng},
booktitle={2023 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR)},
title={Virtual Sparse Convolution for Multimodal
3D Object Detection},
year={2023},
volume={},
number={},
pages={21653-21662},
keywords={Computer vision;Three-dimensional
displays;Laser radar;Image
coding;Convolution;Fuses;Pipelines;Recognition:
Categorization;detection;retrieval},
doi={10.1109/CVPR52729.2023.02074}}

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 81.56 % 78.63 % 85.19 % 82.39 % 87.13 % 88.70 % 90.49 % 88.05 %

Benchmark TP FP FN
CAR 31885 2507 634

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.54 % 86.83 % 90.87 % 113 78.33 %

Benchmark MT rate PT rate ML rate FRAG
CAR 84.77 % 7.08 % 8.15 % 77

Benchmark # Dets # Tracks
CAR 32519 721

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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