Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles

Meng Yin Fu; Fang Yu Liu; Yi Yang; Mei Ling Wang

doi:10.1109/ChiCC.2014.6896705

Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles

Meng Yin Fu^*, Fang Yu Liu, Yi Yang, Mei Ling Wang

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

In this paper, background pixels mutation detection and Hu invariant moments based traffic signs segmentation are combined in traffic signs detection. Considering the gray histogram information in S space, it has good segmentation effects as a global threshold selection method, which can greatly reduce the processing time of the subsequent work. Then using moment invariant theory to extract standard images and seven Hu invariant moments of traffic signs, we contrast the eigenvalues with the suspected areas to establish a rapid and reliable traffic signs detection method.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control Conference, CCC 2014
Editors	Shengyuan Xu, Qianchuan Zhao
Publisher	IEEE Computer Society
Pages	670-674
Number of pages	5
ISBN (Electronic)	9789881563842
DOIs	https://doi.org/10.1109/ChiCC.2014.6896705
Publication status	Published - 11 Sept 2014
Event	Proceedings of the 33rd Chinese Control Conference, CCC 2014 - Nanjing, China Duration: 28 Jul 2014 → 30 Jul 2014

Publication series

Name	Proceedings of the 33rd Chinese Control Conference, CCC 2014
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	Proceedings of the 33rd Chinese Control Conference, CCC 2014
Country/Territory	China
City	Nanjing
Period	28/07/14 → 30/07/14

Keywords

Background pixels mutation detection
Eigenvalues extraction
Hu invariant moments
Traffic signs

Access to Document

10.1109/ChiCC.2014.6896705

Cite this

Fu, M. Y., Liu, F. Y., Yang, Y., & Wang, M. L. (2014). Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles. In S. Xu, & Q. Zhao (Eds.), Proceedings of the 33rd Chinese Control Conference, CCC 2014 (pp. 670-674). Article 6896705 (Proceedings of the 33rd Chinese Control Conference, CCC 2014). IEEE Computer Society. https://doi.org/10.1109/ChiCC.2014.6896705

Fu, Meng Yin ; Liu, Fang Yu ; Yang, Yi et al. / Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles. Proceedings of the 33rd Chinese Control Conference, CCC 2014. editor / Shengyuan Xu ; Qianchuan Zhao. IEEE Computer Society, 2014. pp. 670-674 (Proceedings of the 33rd Chinese Control Conference, CCC 2014).

@inproceedings{264be1e62e0c4f58a733f12e5d40b837,

title = "Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles",

abstract = "In this paper, background pixels mutation detection and Hu invariant moments based traffic signs segmentation are combined in traffic signs detection. Considering the gray histogram information in S space, it has good segmentation effects as a global threshold selection method, which can greatly reduce the processing time of the subsequent work. Then using moment invariant theory to extract standard images and seven Hu invariant moments of traffic signs, we contrast the eigenvalues with the suspected areas to establish a rapid and reliable traffic signs detection method.",

keywords = "Background pixels mutation detection, Eigenvalues extraction, Hu invariant moments, Traffic signs",

author = "Fu, {Meng Yin} and Liu, {Fang Yu} and Yi Yang and Wang, {Mei Ling}",

note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",

year = "2014",

month = sep,

day = "11",

doi = "10.1109/ChiCC.2014.6896705",

language = "English",

series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",

publisher = "IEEE Computer Society",

pages = "670--674",

editor = "Shengyuan Xu and Qianchuan Zhao",

booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",

address = "United States",

}

Fu, MY, Liu, FY, Yang, Y & Wang, ML 2014, Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles. in S Xu & Q Zhao (eds), Proceedings of the 33rd Chinese Control Conference, CCC 2014., 6896705, Proceedings of the 33rd Chinese Control Conference, CCC 2014, IEEE Computer Society, pp. 670-674, Proceedings of the 33rd Chinese Control Conference, CCC 2014, Nanjing, China, 28/07/14. https://doi.org/10.1109/ChiCC.2014.6896705

Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles. / Fu, Meng Yin; Liu, Fang Yu; Yang, Yi et al.
Proceedings of the 33rd Chinese Control Conference, CCC 2014. ed. / Shengyuan Xu; Qianchuan Zhao. IEEE Computer Society, 2014. p. 670-674 6896705 (Proceedings of the 33rd Chinese Control Conference, CCC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles

AU - Fu, Meng Yin

AU - Liu, Fang Yu

AU - Yang, Yi

AU - Wang, Mei Ling

PY - 2014/9/11

Y1 - 2014/9/11

N2 - In this paper, background pixels mutation detection and Hu invariant moments based traffic signs segmentation are combined in traffic signs detection. Considering the gray histogram information in S space, it has good segmentation effects as a global threshold selection method, which can greatly reduce the processing time of the subsequent work. Then using moment invariant theory to extract standard images and seven Hu invariant moments of traffic signs, we contrast the eigenvalues with the suspected areas to establish a rapid and reliable traffic signs detection method.

AB - In this paper, background pixels mutation detection and Hu invariant moments based traffic signs segmentation are combined in traffic signs detection. Considering the gray histogram information in S space, it has good segmentation effects as a global threshold selection method, which can greatly reduce the processing time of the subsequent work. Then using moment invariant theory to extract standard images and seven Hu invariant moments of traffic signs, we contrast the eigenvalues with the suspected areas to establish a rapid and reliable traffic signs detection method.

KW - Background pixels mutation detection

KW - Eigenvalues extraction

KW - Hu invariant moments

KW - Traffic signs

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U2 - 10.1109/ChiCC.2014.6896705

DO - 10.1109/ChiCC.2014.6896705

M3 - Conference contribution

AN - SCOPUS:84907938013

T3 - Proceedings of the 33rd Chinese Control Conference, CCC 2014

SP - 670

EP - 674

BT - Proceedings of the 33rd Chinese Control Conference, CCC 2014

A2 - Xu, Shengyuan

A2 - Zhao, Qianchuan

PB - IEEE Computer Society

T2 - Proceedings of the 33rd Chinese Control Conference, CCC 2014

Y2 - 28 July 2014 through 30 July 2014

ER -

Fu MY, Liu FY, Yang Y , Wang ML. Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles. In Xu S, Zhao Q, editors, Proceedings of the 33rd Chinese Control Conference, CCC 2014. IEEE Computer Society. 2014. p. 670-674. 6896705. (Proceedings of the 33rd Chinese Control Conference, CCC 2014). doi: 10.1109/ChiCC.2014.6896705

Background pixels mutation detection and Hu invariant moments based traffic signs detection on autonomous vehicles

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