People Detection under Occlusion in Multiple Camera Views

This paper proposes a method to locate people on a reference plane using multiple cameras. Previous works rely on people trajectories and color models to solve occlusion.This new approach solves people detection under occlusion by accumulating evidence from multiple views instantaneously and does not rely on previous segmentation of individuals in foreground data or any tracking information.First, foreground data from one view, segmented using background subtraction, is projected onto the ground plane or reference image. The projected foreground of a second view overlaps the first projected foreground only on the points where the foreground intersects the ground plane.Thus, by accumulating the evidence from multiple views,people can be located by detecting local maxima on the accumulated reference image. Experimental results using publicly available data from PETSpsila06 [9] show that the method robustly locates people in very challenging situations with occlusion in most of the views. The locations on the ground plane can further be used for segmentation and tracking on each camera view under severe occlusion.