Robust Vehicle Detection in Vision Systems Based on Fast Wavelet Transform and Texture Analysis
Moving vehicle detection in digital image sequences is one of the key technologies of Intelligent Transportation Systems (ITS). However, problems arise due to the shadows of sunshine in daytime and the illuminations of vehicle headlights in nighttime. To begin with, a new autoregression algorithm based on Gaussian Distribution hypotheses is proposed for background estimation. Furthermore, a pivot approach to eliminate shadows and illuminations from the foreground, which is the difference between dynamic image and background image, is investigated and studied. And in this proposed approach, image textures are extracted by fast wavelet transform (FWT) which is designed for discrete signal while grey level co-occurrence matrix (GLCM) is employed to measure and analyze the extracted textures. Subsequently, shadows and illuminations can be segmented since their textures differ from those of vehicles. Experiment results in real traffic scenes reveal that the techniques presented in this work are effective and efficient for vehicle detection.
Vehicle detection Texture analysis Elimination of shadow and illumination Fast wavelet transform Gray cooccurrence matrix
Peiqun Lin Jianmin Xu Jianyong Bian
College of Traffic & Communication South China University of Technology Guangzhou, Guangdong Province,510640, China
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）