A knowledge-based camera selection approach for object tracking in large sensor networks

In this paper an approach for dynamic sensor 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 the environment model. Hereby, a special goal of this algorithm is to determine the minimum number of sensors needed to relocate an object, even if the object is temporarily out of sight (e.g., by non-overlapping sensor coverage). It will be shown that the algorithm enables self-organizing tracking approaches to perform optimal camera selection in a highly efficient way. In particular, the approach is applicable to very large camera networks and leads to a highly reduced network and processor load for multi-camera tracking.