Geometry-Based Object Association and Consistent Labeling in Multi-Camera Surveillance

This paper proposes a multi-camera surveillance framework based on multiple view geometry. We address the problem of object association and consistent labeling through exploring geometrical correspondences of objects, not only in sequential frames from a single camera view but also across multiple camera views. The cameras are geometrically related through joint combination of multi-camera calibration, ground plane homography constraint, and field-of-view lines. Object detection is implemented using an adaptive Gaussian mixture model, and thereafter the information obtained from different cameras is fused so that the same object shown in different views can be assigned a unique label. Meanwhile, a virtual top-view of ground plane is synthesized to explicitly display the corresponding location and label of each detected object within a designated area-of-interest.