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CLC number: TP317.4; TP391

On-line Access: 2013-11-06

Received: 2013-04-14

Revision Accepted: 2013-07-15

Crosschecked: 2013-10-15

Cited: 3

Clicked: 7248

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.11 P.822-834


Road model prediction based unstructured road detection

Author(s):  Wen-hui Zuo, Tuo-zhong Yao

Affiliation(s):  Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   thomasyao@zju.edu.cn

Key Words:  Road detection, Surface layout, Road model prediction, Temporal smoothing

Wen-hui Zuo, Tuo-zhong Yao. Road model prediction based unstructured road detection[J]. Journal of Zhejiang University Science C, 2013, 14(11): 822-834.

@article{title="Road model prediction based unstructured road detection",
author="Wen-hui Zuo, Tuo-zhong Yao",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Road model prediction based unstructured road detection
%A Wen-hui Zuo
%A Tuo-zhong Yao
%J Journal of Zhejiang University SCIENCE C
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%N 11
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%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300090

T1 - Road model prediction based unstructured road detection
A1 - Wen-hui Zuo
A1 - Tuo-zhong Yao
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 11
SP - 822
EP - 834
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1300090

Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots. In outdoor unstructured environments such as villages and deserts, the roads are usually not well-paved and have variant colors or texture distributions. Traditional region- or edge-based approaches, however, are effective only in specific environments, and most of them have weak adaptability to varying road types and appearances. In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images. The main difference between our proposed algorithm and previous ones is that, before road detection, an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model. This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road. Moreover, a temporal smoothing mechanism is incorporated, which further makes both model prediction and region classification more stable. Experimental results demonstrate that compared with traditional region- and edge-based algorithms, our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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