Road detection and classification in urban environments using conditional random field models

Jyun Fan Tsai; Shih Shinh Huang; Yi Ming Chan; Chan Yu Huang; Li Chen Fu; Pei Yung Hsiao

doi:10.1109/ITSC.2006.1706869

Road detection and classification in urban environments using conditional random field models

Jyun Fan Tsai^*
, Shih Shinh Huang
, Yi Ming Chan
, Chan Yu Huang
, Li Chen Fu
, Pei Yung Hsiao

^*Corresponding author for this work

National Taiwan University
Chang Gung University

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

4 Scopus citations

Abstract

Understanding the road scene structure is essential and important for perceiving the driving situation in intelligent transportation systems (ITS). In this paper, we aim at analyzing the road scene structure by classifying the pixels to three different types, including road surface, lane markings, and non-road objects. Instead of detecting these three objects separately in traditional approaches, we integrate different ad hoc methods under the conditional random field framework. Three feature functions based on three cues including smoothness, color and lane marking segmentation, are used for pixel classification. Besides, an optimization algorithm using graph cuts is applied to find the solutions efficiently. Experiments on the data sets demonstrate high classification accuracy on objects in the road scene.

Original language	English
Title of host publication	Proceedings of ITSC 2006
Subtitle of host publication	2006 IEEE Intelligent Transportation Systems Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	963-967
Number of pages	5
ISBN (Print)	1424400945, 9781424400942
DOIs	https://doi.org/10.1109/ITSC.2006.1706869
State	Published - 2006
Externally published	Yes
Event	2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006 - Toronto, ON, Canada Duration: 17 09 2006 → 20 09 2006

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)	2153-0009
ISSN (Electronic)	2153-0017

Conference

Conference	2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006
Country/Territory	Canada
City	Toronto, ON
Period	17/09/06 → 20/09/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Access to Document

10.1109/ITSC.2006.1706869

Cite this

Tsai, J. F., Huang, S. S., Chan, Y. M., Huang, C. Y., Fu, L. C., & Hsiao, P. Y. (2006). Road detection and classification in urban environments using conditional random field models. In Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference (pp. 963-967). Article 1706869 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2006.1706869

Tsai, Jyun Fan ; Huang, Shih Shinh ; Chan, Yi Ming et al. / Road detection and classification in urban environments using conditional random field models. Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. Institute of Electrical and Electronics Engineers Inc., 2006. pp. 963-967 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

@inproceedings{8dbf68e54b8d4b58ab52f4a636fd2607,

title = "Road detection and classification in urban environments using conditional random field models",

abstract = "Understanding the road scene structure is essential and important for perceiving the driving situation in intelligent transportation systems (ITS). In this paper, we aim at analyzing the road scene structure by classifying the pixels to three different types, including road surface, lane markings, and non-road objects. Instead of detecting these three objects separately in traditional approaches, we integrate different ad hoc methods under the conditional random field framework. Three feature functions based on three cues including smoothness, color and lane marking segmentation, are used for pixel classification. Besides, an optimization algorithm using graph cuts is applied to find the solutions efficiently. Experiments on the data sets demonstrate high classification accuracy on objects in the road scene.",

author = "Tsai, \{Jyun Fan\} and Huang, \{Shih Shinh\} and Chan, \{Yi Ming\} and Huang, \{Chan Yu\} and Fu, \{Li Chen\} and Hsiao, \{Pei Yung\}",

year = "2006",

doi = "10.1109/ITSC.2006.1706869",

language = "英语",

isbn = "1424400945",

series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "963--967",

booktitle = "Proceedings of ITSC 2006",

address = "美国",

note = "2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006 ; Conference date: 17-09-2006 Through 20-09-2006",

}

Tsai, JF, Huang, SS, Chan, YM, Huang, CY, Fu, LC & Hsiao, PY 2006, Road detection and classification in urban environments using conditional random field models. in Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference., 1706869, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Institute of Electrical and Electronics Engineers Inc., pp. 963-967, 2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006, Toronto, ON, Canada, 17/09/06. https://doi.org/10.1109/ITSC.2006.1706869

Road detection and classification in urban environments using conditional random field models. / Tsai, Jyun Fan; Huang, Shih Shinh; Chan, Yi Ming et al.
Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. Institute of Electrical and Electronics Engineers Inc., 2006. p. 963-967 1706869 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

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

TY - GEN

T1 - Road detection and classification in urban environments using conditional random field models

AU - Tsai, Jyun Fan

AU - Huang, Shih Shinh

AU - Chan, Yi Ming

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AU - Fu, Li Chen

AU - Hsiao, Pei Yung

PY - 2006

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N2 - Understanding the road scene structure is essential and important for perceiving the driving situation in intelligent transportation systems (ITS). In this paper, we aim at analyzing the road scene structure by classifying the pixels to three different types, including road surface, lane markings, and non-road objects. Instead of detecting these three objects separately in traditional approaches, we integrate different ad hoc methods under the conditional random field framework. Three feature functions based on three cues including smoothness, color and lane marking segmentation, are used for pixel classification. Besides, an optimization algorithm using graph cuts is applied to find the solutions efficiently. Experiments on the data sets demonstrate high classification accuracy on objects in the road scene.

AB - Understanding the road scene structure is essential and important for perceiving the driving situation in intelligent transportation systems (ITS). In this paper, we aim at analyzing the road scene structure by classifying the pixels to three different types, including road surface, lane markings, and non-road objects. Instead of detecting these three objects separately in traditional approaches, we integrate different ad hoc methods under the conditional random field framework. Three feature functions based on three cues including smoothness, color and lane marking segmentation, are used for pixel classification. Besides, an optimization algorithm using graph cuts is applied to find the solutions efficiently. Experiments on the data sets demonstrate high classification accuracy on objects in the road scene.

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U2 - 10.1109/ITSC.2006.1706869

DO - 10.1109/ITSC.2006.1706869

M3 - 会议稿件

AN - SCOPUS:41849111008

SN - 1424400945

SN - 9781424400942

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 963

EP - 967

BT - Proceedings of ITSC 2006

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2006 IEEE Intelligent Transportation Systems Conference, ITSC 2006

Y2 - 17 September 2006 through 20 September 2006

ER -

Tsai JF, Huang SS, Chan YM, Huang CY, Fu LC, Hsiao PY. Road detection and classification in urban environments using conditional random field models. In Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. Institute of Electrical and Electronics Engineers Inc. 2006. p. 963-967. 1706869. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/ITSC.2006.1706869

Road detection and classification in urban environments using conditional random field models

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