Study Of Pedestrian Detection Based On Computer Vision For Driver Assistance Systems

The technique of moving pedestrian detection is one of the key techniques for Intelligent Vehicle Navigation System and is coming to a hot spot of research in computer application field at present. It is in the Intelligent Vehicle assisted navigation technology bottom, splitting the moving objects real-time and effectively is essential foundation for the subsequent processing, including target identification, tracking behavior comprehension. Method of moving pedestrian detection will also be widely used in the intelligent control system, virtual reality, robot applications.This paper is study on the Computer Vision Based Pedestrian Detection for Driver Assistance Systems. First of all, a detailed analysis of video images based on the moving target detection methods, the dissertation mainly studied the background subtraction, and improved background modeling method, considering the pixels neighboring relativity, the algorithm through the interval pixels establishment of Gaussian mixture model instead of the traditional point by point establishes Gaussian mixture model in background subtraction. In order to overcome the lack of sensitivity of light change, combining the adaptive background subtraction with the symmetrical differencing obtains the integrity foreground image. Then, during the process of matching, the template for pedestrian matching and recognizing in image sequence was presented by fusing several characters of targets. Finally, on the basis of moving pedestrian segmentation in this paper, continue to process the Video images, combined with Kalman filter estimates the position of the pedestrian bounding box, A dynamic and variable area of interest is formed to narrow the seek range of pedestrian .In the experiments, used hand-held video camera to replace the video camera equipped on the moving vehicle to process the images. The experiment results indicate that the algorithms introduced could achieve effective recognition of the proceeding pedestrians. At the same time, the algorithms have good tracking. A serial of experiment have been implemented to verify the effectiveness of the method supported by the papers, which can satisfied with the requisition of the real time system and have been more precisely.