文献类型：期刊文献

英文题名：Robust free-space detection in urban roads based on MSER extraction using gradient images

作者：Xin, Le[1,2,3]; Song, Juan[1,4]; Chen, Yangzhou[1,2,3]; Hu, Jiangbi[2,3,5]

第一作者：Xin, Le

机构：[1] College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China; [2] Beijing Key Laboratory of Traffic Engineering, Beijing, 100124, China; [3] Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing, 100124, China; [4] College of Automation, Beijing Union University, Beijing, 100101, China; [5] College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China

第一机构：College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, China

年份：2018

卷号：2018-July

起止页码：4141-4146

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20184606055470)

语种：英文

外文关键词：Roads and streets - Computational complexity - Video signal processing - Extraction

摘要：In this paper, a robust algorithm to detect free-spaces in urban roads using video sequences captured on-board is proposed, mainly aiming at the complex urban road conditions such as the change of road structure and dense traffic conditions, as well as the changing illumination. This algorithm is based on MSER (Maximally Stable Extremal Region) extraction using gradient images, which includes the following two aspects. Firstly, the gradient images are calculated by the Sobel operator formed as the spatial filter template. MSERs are extracted from these gradient images with an improved MSER extraction algorithm with linear complexity. Then some shape analysis is applied on each MSER after shape representation is computed by the contour extraction. On the basis of selecting the proper pixel location of the initial seed point, the region of the road area is determined where the initial seed point falls, based on the assumption that the region directly in front of the host vehicle is just the road area. Experimental results show that this method can run well in various traffic scenarios, and achieve real-time and robust road detection results. ? 2018 Technical Committee on Control Theory, Chinese Association of Automation.