Stereo vision-based pedestrian detection using multiple features for automotive application

In this paper, we propose a stereo vision-based pedestrian detection using multiple features for automotive application. The disparity map from stereo vision system and multiple features are utilized to enhance the pedestrian detection performance. Because the disparity map offers us 3D information, which enable to detect obstacles easily and reduce the overall detection time by removing unnecessary backgrounds. The road feature is extracted from the vdisparity map calculated by the disparity map. The road feature is a decision criterion to determine the presence or absence of obstacles on the road. The obstacle detection is performed by comparing the road feature with all columns in the disparity. The result of obstacle detection is segmented by the bird's-eye-view mapping to separate the obstacle area which has multiple objects into single obstacle area. The histogram-based clustering is performed in the bird's-eye-view map. Each segmented result is verified by the classifier with the training model. To enhance the pedestrian recognition performance, multiple features such as HOG, CSS, symmetry features are utilized. In particular, the symmetry feature is proper to represent the pedestrian standing or walking. The block-based symmetry feature is utilized to minimize the type of image and the best feature among the three symmetry features of H-S-V image is selected as the symmetry feature in each pixel. ETH database is utilized to verify our pedestrian detection algorithm.