Multi-view and multi-plane data fusion for effective pedestrian detection in intelligent visual surveillance

For the robust detection of pedestrians in intelligent video surveillance, an approach to multi-view and multi-plane data fusion is proposed. Through the estimated homography, foreground regions are projected from multiple camera views to a reference view. To identify false-positive detections caused by foreground intersections of non-corresponding objects, the homographic transformations for a set of parallel planes, which are from the head plane to the ground, are applied. Multiple features including occupancy information and colour cues are extracted from such planes for joint decision-making. Experimental results on real world sequences have demonstrated the good performance of the proposed approach in pedestrian detection for intelligent visual surveillance.