Human Tracking based on Multiple View Homography

We propose a method for detection and tracking for objects under multiple cameras system. To track objects, one need to establish correspondence objects among multiple views. We apply the principal axis of objects and the homography constraint to match objects across multiple cameras. The principal axis belongs to the silhouette of objects that is extracted by the background subtraction. We use the multiple background model to the background subtraction. In an image sequence, many changes happen with respect to pixel intensity. This cannot be characterized by the single background model so that is necessary to use the multiple background model. Also, we use the median background model reducing some noises. The silhouette is detected by difference with background models and current image which includes moving objects. For calculating homography, we use landmarks on the ground plane in 3D space. The homography means the relation between two correspondence between two coinciding points from different views. The intersection of principal axes and ground plane in 3D space are the same point shown in each view. The intersection occurs when a principal axis in an image crosses to the transformed ground plane from another image. We construct the correspondence which means the relationship between intersection in current image and transformed intersection from the other image by homography constraint. Those correspondences confirm within a short distance measuring in the top viewed plane. Thus, we track a person by these corresponding points on the ground plane.