After extracting the candidate region from an image, it is necessary to take a kind of technology to determine whether the split target is a pedestrian. By analysis and feature extraction to segmentations of pedestrian candidate region, the classification of pedestrians has been studied. The pedestrian classifier of the SVM (Support Vector Machines) has been trained with pedestrian's typical characteristics. This paper mainly studies the efficient algorithms of splitting pedestrian target from other non-pedestrians. As the pedestrians in the image will show different shapes, postures and sizes, and they are usually in different light conditions, it is complicate to describe the pedestrians. This paper proposes a pedestrian segmentation method, which effectively solves the problems, making the classifier be able to deal with the complicate problems. Secondly, the paper uses the pedestrian image texture and shape features to describe the pedestrian. The extracted features are taken as the input of SVM. In order to solve the impact of lighting and other factors to pedestrian recognition, some characteristics have been considered, such as the pedestrian's grayscale images have certain gray symmetry and texture features, and the pedestrians successive edge makes outline of the image features clear. By using lots of events to train the SVM algorithm the recognized pedestrian classification can be obtained. The test results show that the proposed algorithm can effectively recognize different pedestrians in front of the vehicle and get a good real-time effect.
展开▼
