SELF-ORGANIZED AND SCALABLE CAMERA NETWORKS FOR SYSTEMATIC HUMAN TRACKING ACROSS NONOVERLAPPING CAMERAS

摘要

We present a self-organized and scalable multiple-camera tracking system that tracks human across the cameras with nonoverlapping views. Given the GPS locations of uncalibrated cameras, the system automatically detects the existence of camera link within the camera network based on the routing information provided by Google Maps. The connected zones in any pair of directly-connected cameras are identified based on the feature matching between the camera's view and Google Street View. The camera link model is further estimated by an unsupervised learning scheme. Finally, multiple-camera tracking is performed. Thanks to the unsupervised pairwise learning and tracking in our system, the camera network is self-organized, and our proposed system is able to be scaled up efficiently when more cameras are added into the network.