UV disparity based obstacle detection and pedestrian classification in urban traffic scenarios

High accuracy pedestrian detection plays an important role in all intelligent vehicles. This paper describes a system for detecting the obstacles in front of the vehicle and classifying them in pedestrians and non-pedestrians. It acquires the traffic scenes using a low-cost pair of gray intensities stereo cameras. A SORT-SGM stereo-reconstruction technique is used in order to obtain high density and accuracy in stereo-reconstructed points. First, the road plane is computed using the V disparity map and then the obstacles are determined by analyzing the U disparity map. Size related and histogram of oriented gradient based on gray levels features are used for describing each pedestrian hypothesis. A principle component analysis on the features is used for their selection and projection in a relevant space. Different SVM classifiers are trained considering the relevant features on large pedestrian and non-pedestrian image sets. A comparison between them is finally performed for selecting the one that achieves the best classification score.