Vehicle Detection Under Urban Road Circumstance

The detection of the forward steering vehicles is one of the key tasks of the information perception process in Intelligent Transportation System. In this thesis, we investigate into the vehicle detection under the complicated traffic scenarios of urban roads and present a monocular-based method applying to vehicle detection.In the vehicle detection section, we first adopt a three-rank pyramidal Gauss filter to smooth the original image and use a Sobel 3*3 operator to gain the vertical edges of vehicle contour as global feature. After the imaging width threshold of vehicles is calculated, a so-called"voting"mechanism is applied to map the symmetrical axes of edges into a 1-D vector. After the study of experiment data and relevant academic technologies, we detect the vehicle symmetry axes based on Wavelet Transform Module Maximum(WTMM) theory. In order to generate the Region of Interests(ROI) for latter vehicle validation, we combine multi-features of vehicle as judgment bases to detect its external rectangle. Horizontal contour symmetry, bottom shadow and shape scale are utilized respectively to detect the left, right, bottom and upper boundary. Then, vehicle validation process is accomplished by local entropy and local gray scale symmetry estimate. Finally, The validated ROI area is filtered to obtain the ultimate detection results.The algorithm was tested under different traffic scenarios (e.g., simply structured highway, complex urban street, varying sunlight conditions), especially under the complicated traffic circumstance of urban roads. The approving experimental results show that the method is effective, stabilized and robust. Additionally, it is fast enough to be used in real-time Intelligent Transportation System. Thus, the method could be well used in vehicle detection under urban traffic scenarios.