Dynamic Sensor Selection for Single Target Tracking in Large Video Surveillance Networks

In this paper an approach for dynamic camera selection in large video-based sensor networks for the purpose of multi-camera object tracking is presented. The sensor selection approach is based on computational geometry algorithms and is able to determine task-relevant cameras (camera cluster) by evaluation of geometrical attributes, given the last observed object position, the sensor configurations and a building map. A special goal of this algorithm is the efficient determination of the minimum number of sensors needed to relocate an object, even if the object is temporarily out of sight. In particular, the approach is applicable in camera networks with overlapping and non-overlapping field of views as well as with static and non-static sensors.