Research On Vehicle Detection Technology Through The Combination Of Color, Texture And Shape Priors

With the development of city and economy and the improvement of the people's living standard, the number of vehicles on the road increased quickly, and the burden of the road transportation system becomes higher and higher. Therefore, more attention has been paid to the vision-based intelligent transportation system. In this paper, we do some researches on vehicle detection, which is the key technology of the intelligent transportation system, and propose algorithms on background extraction and update, and shadow suppression. Research works in this paper are summarized as follows:Firstly, we propose a background extraction and update method based on block features by improving an existing algorithm. The method divides images into blocks, and computes statistical likelihood for each block in the time domain. Blocks with slight change are classified as the static blocks and put into a buffer. These blocks are used to extract and update the background image. Compared with the background model based on pixel, this method can reduce the computing complex significantly, and can also get good result.Secondly, we present an algorithm to do vehicle segmentation and cast shadow removal using the color and texture information. In the first step, the method uses color information to detect shadows in objects. In the second step, texture features are used to find edges of vehicles and shadows. Then we combine characters of the shadow edges and the result in the first stage to remove the shadow edges. Experimental results show that the method can cope with shadows in any directions. But for a few of vehicles, this method still can't obtain the whole contour.Finally, a vehicle detection method based on the prior shape knowledge is proposed. This method uses the result of the shadow removal algorithm as the initial contour, and restores the vehicle contour with prior shape knowledge. An implicit shape model is built and an active contour energy function with the restriction of the existing shape priors is constructed. Then we apply the shape alignment and level set method to evolving the initial contour until convergence. Results in the experiments show that we can overcome the defect of the shadow removal algorithm using this method and get the precise contours of vehicles.