Pedestrian detection system based on stereo vision for mobile robot

This paper presents a novel Graphics Processing Unit (GPU)-based system for pedestrian detection with stereo vision in real images on mobile robot. The process of obtaining a dense disparity map on a GPU for real-time applications and the edge property of the scene to extract a region of interest (ROI) is designed. After extracting the histograms of the oriented gradients on the ROIs, a support vector machine (SVM) classifies them as pedestrian and non-pedestrian types. The system employs the recognition by components method, which compensates for the pose and articulation changes of pedestrians. In order to effectively track spatial pedestrian estimates over sequences, sub-windows in distinctive parts of human beings are used as measurements of the Kalman filter.