Road detection based on scene structural knowledge and CRF

被引：0

作者：

Deng Y. ^{[1
]}

Lu Z. ^{[1
]}

Li J. ^{[1
]}

机构：

[1] School of Telecommunications Engineering, Xidian University, Xi'an

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2016年 / 44卷 / 09期

关键词：

Fully connected condition random field; Multi-class regression; Road detection; Scene structural layout; Super pixels; Vanishing point;

D O I：

10.13245/j.hust.160905

中图分类号：

学科分类号：

摘要：

Existing approaches classify the road area by using appearance-based features, which is vulnerable to the complicated imaging conditions such as extreme shadows, illumination and occlusion. A new road detection method was proposed to overcome this problem brought about by these factors, which combined the advantages of the structural knowledge and fully connected conditional random fields (CRFs). Firstly, a confidence map of road was generated based on the detection of the vanishing point and road boundaries. Secondly, a scene layout map was estimated by training a regression model using superpixel features. Two maps and appearance features were used to calculate the energy function of the CRF. Finally, road pixels could be labeled using an efficient inference method. Experimental results prove the effectiveness of the usage of structure information and a fully connected CRF, and the proposed method is robust to the shadows and occlusion in real road scenes. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：20 / 25

页数：5

共 16 条

[1]

Gregor R., Lutzeler M., Pellkofer M., Et al., EMS-Vision: a perceptual system for autonomous vehicles, IEEE Transactions on Intelligent Transportation Systems, 3, 1, pp. 48-59, (2002)

[2]

Zhang G., Zheng N., Cui C., Et al., An efficient road detection method in noisy urban environment, Proc of IEEE Intelligent Vehicles Symposium, pp. 556-561, (2009)

[3]

Loose H., Franke U., B-spline-based road model for 3D lane recognition, Proc of Intelligent Transportation Systems (ITSC), pp. 91-98, (2010)

[4]

Bertozzi M., Bombini L., Broggi A., Et al., Gold: a frame work for developing intelligent-vehicle vision applications, Intelligent Systems, 23, 1, pp. 69-71, (2008)

[5]

Kong H., Augibert Y.J., Ponce J., Vanishing point detection for road detection, Proc of Computer Vision and Pattern Recognition, pp. 96-103, (2009)

[6]

Gould S., DARWIN: a framework for machine learning and computer vision research and development, Journal of Machine Learning Research, 13, 1, pp. 3533-3537, (2012)

[7]

Alvarez J.M., Antonio M.L., Road detection based on illuminant invariance, IEEE Transactions on Intelligent Transportation Systems, 12, 1, pp. 184-193, (2011)

[8]

Alvarez J.M., Antonio M.L., Gevers T., Et al., Combining priors, appearance, and context for road detection, IEEE Transactions on Intelligent Transportation Systems, 15, 3, pp. 1168-1178, (2014)

[9]

Hoiem D., Efros A.A., Hebert M., Recovering surface layout from an image, International Journal of Computer Vision, 75, 1, pp. 151-172, (2007)

[10]

Shotton J., Johnson M., Cipolla R., Semantic texton forests for image categorization and segmentation, Proc of Computer Vision and Pattern Recognition, pp. 1-8, (2008)

← 1 2 →