Detecting and recognizing moving pedestrians in video

Detecting and recognizing pedestrians in video footages are two essential and significant tasks in many automatic video understanding systems. In this paper, we propose an efficient approach to moving pedestrian detection and recognition in video. The testing process of this approach involves two main steps: moving edge detection and hypotheses generation. Moving edges are firstly extracted by comparing the edges identified in adjacent frames. Shape context descriptors are then produced for the edge points sampled from the moving edges and matched against the instances of a codebook that is learned from a set of training samples to generate initial hypotheses. Final hypotheses are formed by pruning initial hypotheses with large overlaps. Experiments with a publicly available dataset show that the proposed approach can reliably detect and recognize moving pedestrians in real scenes that contain either different viewing angles or different degrees of occlusions.