Study On Detection Of Pedestrian Characteristics Based On Vision

With the rapid development of human society and the increasing vehicles, the road traffic accidents which occurred frequently cause great damages. How to reduce the traffic accidents and the corresponding loss has become a hot topic. The intelligent assisted driving is one effective way to solve the problem. Pedestrian detection is the key technique for intelligent assisted driving. It detects the target area for the presence of pedestrian by the method. It is combining the technology of sensors, image processing, computer, and so on.This paper is study on machine vision of pedestrians detection technology. Firstly, the degradation model of motion-blurred image was analysed. The method of imaging the motion-blurred image for pedestrian detection and the main factors about image blurring were derived. The wiener filter has better image processing effect by the experiments. Secondly, the pedestrian profile was researched. The method of edge detection based on Canny has a good image edge detection effect by the experiments. Then combining with grayscale morphological for image processing. The results show that the method can be used to eliminate the interference factors of pedestrian profile. Finally, support vector machine was analysed, and support vector machine classifier was structured. Extracting test data from sample image by the texture feature and invariant moments feature. The test sample was recognized by the SVM classifier that was trained by the part of sample image. The experimental results show that these two kinds of features of pedestrians could achieve effectively recognition of the pedestrians.In the experiments, the images were obtained by using hand-held video camera to replace the vehicle-mounted CCD. The image database of pedestrians and non-pedestrians was established by the collected images that were processed simply.