A comprehensive approach for road marking detection and recognition

被引:7
作者
Ding, Ling [1 ,2 ]
Zhang, Huyin [1 ]
Xiao, Jinsheng [3 ]
Li, Bijun [4 ]
Lu, Shejie [2 ]
Klette, Reinhard [5 ]
Norouzifard, Mohammad [5 ]
Xu, Fang [6 ]
机构
[1] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
[2] Hubei Univ Sci & Technol, Coll Comp Sci & Technol, Xianning 437100, Peoples R China
[3] Wuhan Univ, Sch Elect Informat, Wuhan 430072, Peoples R China
[4] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[5] Auckland Univ Technol, Sch Engn Comp & Math Sci, EEE Dept, Auckland 1142, New Zealand
[6] Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan 432000, Peoples R China
基金
中国国家自然科学基金;
关键词
Road markings; Object detection; Image segmentation; PCA-HOG; SVM; STEREO;
D O I
10.1007/s11042-019-08384-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a comprehensive approach for road marking detection and recognition which collect the image information from a camera mounted in the front of the vehicle. This approach uses PCA-HOG feature and support vector machine (SVM) to detect and recognize the road markings. First, the vanishing point in the image is identified, and is used to generate a bird's-eye view of the road. Second, the method of local median binarization is used to segment the image and it can avoid the interference of light and shadow. Finally, the feature vector is generated by using a combination of HOG and PCA, and the SVM is used to determine the feature vector as a specific road marking. The test results show that this algorithm higher accuracy and recall rate. Moreover, the algorithm has low computational complexity and can guarantee high recognition speed. Therefore, compared with existing algorithms, it has great advantages. This research will play an important role and a wide range of applications in advanced driver assistant system (ADAS) or driverless driving.
引用
收藏
页码:17193 / 17210
页数:18
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