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
收藏
页码:20 / 25
页数:5
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