Integrated real-time vision-based preceding vehicle detection in urban roads

Yanwen Chong; Wu Chen; Zhilin Li; Hing Keung William Lam; Qingquan Li

doi:10.1007/978-3-642-24728-6_36

Integrated real-time vision-based preceding vehicle detection in urban roads

Yanwen Chong, Wu Chen, Zhilin Li, Hing Keung William Lam, Qingquan Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

This paper presents a real-time algorithm for a vision-based preceding vehicle detection system. The algorithm contains two main components: vehicle detection with various vehicle features, and vehicle detection verification with dynamic tracking. Vehicle detection is achieved using vehicle shadow features to define a region of interest (ROI). After utilizing methods such as histogram equalization, ROI entropy and mean of edge image, the exact vehicle rear box is determined. In the vehicle tracking process, the predicted box is verified and updated. Test results demonstrate that the new system possesses good detection accuracy and can be implemented in real-time operation.

Original language	English
Title of host publication	Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
Pages	270-275
Number of pages	6
DOIs	https://doi.org/10.1007/978-3-642-24728-6_36
Publication status	Published - 1 Dec 2011
Event	7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China Duration: 11 Aug 2011 → 14 Aug 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6838 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Intelligent Computing, ICIC 2011
Country/Territory	China
City	Zhengzhou
Period	11/08/11 → 14/08/11

Keywords

Feature extraction
Shadow boundary
Vehicle detection
Vision tracking

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-642-24728-6_36

Cite this

Chong, Y., Chen, W., Li, Z., Lam, H. K. W., & Li, Q. (2011). Integrated real-time vision-based preceding vehicle detection in urban roads. In Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers (pp. 270-275). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6838 LNCS). https://doi.org/10.1007/978-3-642-24728-6_36

Chong, Yanwen ; Chen, Wu ; Li, Zhilin et al. / Integrated real-time vision-based preceding vehicle detection in urban roads. Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers. 2011. pp. 270-275 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c203fa9b13f1404db0f466e33db4ba9a,

title = "Integrated real-time vision-based preceding vehicle detection in urban roads",

abstract = "This paper presents a real-time algorithm for a vision-based preceding vehicle detection system. The algorithm contains two main components: vehicle detection with various vehicle features, and vehicle detection verification with dynamic tracking. Vehicle detection is achieved using vehicle shadow features to define a region of interest (ROI). After utilizing methods such as histogram equalization, ROI entropy and mean of edge image, the exact vehicle rear box is determined. In the vehicle tracking process, the predicted box is verified and updated. Test results demonstrate that the new system possesses good detection accuracy and can be implemented in real-time operation.",

keywords = "Feature extraction, Shadow boundary, Vehicle detection, Vision tracking",

author = "Yanwen Chong and Wu Chen and Zhilin Li and Lam, {Hing Keung William} and Qingquan Li",

year = "2011",

month = dec,

day = "1",

doi = "10.1007/978-3-642-24728-6_36",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "270--275",

booktitle = "Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers",

note = "7th International Conference on Intelligent Computing, ICIC 2011 ; Conference date: 11-08-2011 Through 14-08-2011",

}

Chong, Y, Chen, W, Li, Z, Lam, HKW & Li, Q 2011, Integrated real-time vision-based preceding vehicle detection in urban roads. in Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6838 LNCS, pp. 270-275, 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, 11/08/11. https://doi.org/10.1007/978-3-642-24728-6_36

Integrated real-time vision-based preceding vehicle detection in urban roads. / Chong, Yanwen; Chen, Wu; Li, Zhilin et al.
Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers. 2011. p. 270-275 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6838 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - This paper presents a real-time algorithm for a vision-based preceding vehicle detection system. The algorithm contains two main components: vehicle detection with various vehicle features, and vehicle detection verification with dynamic tracking. Vehicle detection is achieved using vehicle shadow features to define a region of interest (ROI). After utilizing methods such as histogram equalization, ROI entropy and mean of edge image, the exact vehicle rear box is determined. In the vehicle tracking process, the predicted box is verified and updated. Test results demonstrate that the new system possesses good detection accuracy and can be implemented in real-time operation.

AB - This paper presents a real-time algorithm for a vision-based preceding vehicle detection system. The algorithm contains two main components: vehicle detection with various vehicle features, and vehicle detection verification with dynamic tracking. Vehicle detection is achieved using vehicle shadow features to define a region of interest (ROI). After utilizing methods such as histogram equalization, ROI entropy and mean of edge image, the exact vehicle rear box is determined. In the vehicle tracking process, the predicted box is verified and updated. Test results demonstrate that the new system possesses good detection accuracy and can be implemented in real-time operation.

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KW - Vision tracking

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Chong Y, Chen W, Li Z, Lam HKW, Li Q. Integrated real-time vision-based preceding vehicle detection in urban roads. In Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers. 2011. p. 270-275. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24728-6_36

Integrated real-time vision-based preceding vehicle detection in urban roads

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Keywords

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