Full Text:   <2484>

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: 5435

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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

http://doi.org/10.1631/jzus.C1300090


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",
volume="14",
number="11",
pages="822-834",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300090"
}

%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
%V 14
%N 11
%P 822-834
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300090

TY - JOUR
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
ER -
DOI - 10.1631/jzus.C1300090


Abstract: 
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

Reference

[1]Alon, Y., Ferencz, A., Shashua, A., 2006. Off-Road Path Following Using Region Classification and Geometric Projection Constraints. IEEE Conf. on Computer Vision and Pattern Recognition, p.689-696.

[2]Alvarez, J.M., Lopez, A.M., Baldrich, R., 2008. Illuminant Invariant Model-Based Road Segmentation. Proc. IEEE Intelligent Vehicles Symp., p.1175-1180.

[3]Alvarez, J.M., Gevers, T., Lopez, A.M., 2009. Vision-Based Road Detection Using Road Model. 16th IEEE Int. Conf. on Image Processing, p.2073-2076.

[4]Alvarez, J.M., Gevers, T., Lopez, A.M., 2010a. 3D Scene Priors for Road Detection. IEEE Conf. on Computer Vision and Patten Recognition, p.57-64.

[5]Alvarez, J.M., Lumbreras, F., Gevers, T., Lopez, A.M., 2010b. Geographic Information for Vision-Based Road Detection. IEEE Intelligent Vehicles Symp., p.621-626.

[6]Alvarez, J.M., Gevers, T., LeCun, Y., Lopez, A.M., 2012. Road Scene Segmentation from a Single Image. European Conf. on Computer Vision, p.376-389.

[7]Alvarez, J.M., Gevers, T., Diego, F., Lopez, A.M., 2013. Road geometry classification by adaptive shape models. IEEE Trans. Intell. Transp. Syst., 14(1):459-468.

[8]Bellutta, P., Manduchi, R., Matthies, L., Owens, K., Rankin, A., 2000. Terrain Perception for DEMO III. Proc. IEEE Intelligent Vehicles Symp., p.326-331.

[9]Bertozzi, M., Broggi, A., 1998. GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Process., 7(1):62-81.

[10]Boykov, Y., Veksler, O., Zabih, R., 2001. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell., 23(11):1222-1239.

[11]Dalal, N., Triggs, B., 2005. Histogram of Oriented Gradient for Human Detection. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.886-893.

[12]Duan, K.B., Keerthi, S.S., Chu, W., Shevade, A.K., Poo, A.N., 2003. Multi-category Classification by Soft-Max Combination of Binary Classifiers. 4th Int. Workshop on Multiple Classifier Systems, p.125-134.

[13]Felzenszwalb, P., Huttenlocher, D., 2004. Efficient graph-based image segmentation. Int. J. Comput. Vis., 59(2):167-181.

[14]Guo, C.Z., Mita, S., McAllester, D., 2010. MRF-Based Road Detection with Unsupervised Learning for Autonomous Driving in Changing Environments. IEEE Intelligent Vehicles Symp., p.361-368.

[15]He, Y.H., Wang, H., Zhang, B., 2004. Color based road detection in urban traffic scenes. IEEE Trans. Intell. Transp. Syst., 5(4):309-318.

[16]Hoiem, D., Efros, A.A., Hebert, M., 2007. Recovering surface layout from an image. Int. J. Comput. Vis., 75(1):151-172.

[17]Kelly, A., Stentz, A., Amidi, O., Bode, M., Bradley, D., Diaz-Calderon, A., Happold, M., Herman, H., Mandelbaum, R., Pilarski, T., et al., 2006. Toward reliable off road autonomous vehicles operating in challenging environments. Int. J. Robot. Res., 25(5-6):449-483.

[18]Kong, H., Audibert, J.Y., Ponce, J., 2009. Vanishing Point Detection for Road Detection. IEEE Conf. on Computer Vision and Pattern Recognition, p.96-103.

[19]Lazebnik, S., Schmid, C., Ponce, J., 2006. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.2169-2178.

[20]Lombardi, P., Zanin, M., Messelodi, S., 2005. Switching Models for Vision-Based On-Board Road Detection. IEEE Intelligent Vehicles Symp., p.67-72.

[21]Lookingbill, A., Rogers, J., Lieb, D., Curry, J., Thrun, S., 2007. Reverse optical flow for self-supervised adaptive autonomous robot navigation. Int. J. Comput. Vis., 74(3):287-302.

[22]Lutzeler, M., Dick, E.D., 1998. Road Recognition with Marveye. Proc. IEEE Intelligent-Vehicles Symp., p.341-346.

[23]Moghadam, P., Starzyk, J.A., Wijesoma, W.S., 2012. Fast vanishing point detection in unstructured environments. IEEE Trans. Image Process., 21(1):425-430.

[24]Thorpe, C., Carlson, J., Duggins, D., 2003. Safe Robot Driving in Cluttered Environments. 11th Int. Symp. of Robotics Research, p.271-280.

[25]Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al., 2006. Stanley: the robot that won the DARPA grand challenge. J. Field Robot., 23(9):661-692.

[26]van de Sande, K.E.A., Gevers, T., Snoek, C.G.M., 2010. Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell., 32(9):1582-1596.

[27]Wang, Y., Teoh, E.K., Shen, D.G., 2004. Lane detection and tracking using B-snake. Image Vis. Comput., 22(4):269-280.

[28]Wang, Y., Bai, L., Fairhurst, M., 2008. Robust road modeling and tracking using condensation. IEEE Trans. Intell. Transp. Syst., 9(4):570-579.

[29]Zhang, G., Zheng, N., Cui, C., Yan, Y.Z., Yuan, Z.J., 2009. An Efficient Road Detection Method in Noisy Urban Environment. IEEE Intelligent Vehicles Symp., p.556-561.

[30]Zhou, S.Y., Iagnemma, K., 2010. Self-Supervised Learning Method for Unstructured Road Detection Using Fuzzy Support Vector Machines. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1183-1189.

[31]Zhou, S.Y., Gong, J.W., Xiong, G.G., Chen, H.Y., Iagnemma, K., 2010. Road Detection Using Support Vector Machine Based on Online Learning and Evaluation. IEEE Intelligent Vehicles Symp., p.256-261.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE