Vehicle Detection Using Tail Light Segmentation
This paper presents a method for vehicle detection based on forward looking CCD camera, where vehicle tail light information is employed to generate vehicle candidate. Color segmentation consists of finding pairs of light blobs and removing the isolated points after morphological closing and opening. Among the horizontal light pairs, it determines to define the vehicle candidate. In vehicle candidate verification step, a feature set by Gabor filters using eight direction and five scales is used to train a back propagation neural network (BPNN). In the experiment, this BPNN classifier is used to detect the vehicle. Total 104 images are tested by this algorithm. 87 vehicle images are detected successfully. These results show that our proposed method is effective for vehicle detection in the daytime.
Vehicle Detection back propagation Neural Network Color Segmentaion Gabor Feature
Qing Ming Kang-Hyun Jo
University of Ulsan Ulsan,Korea
国际会议
The 6th International Forum on Strategic Technology(IFOST 2011)(第六届国际战略技术论坛)
哈尔滨
英文
729-732
2011-08-22（万方平台首次上网日期，不代表论文的发表时间）