A decentralized Bayesian algorithm for identification of tracked targets

The problem of identification of objects being tracked by a fully decentralized surveillance system is considered. A decentralized multisensor system is used to track targets (people and mobile robots) as they enter and move around a factory assembly room performing tasks. The sensors used in this system (CCD cameras) reveal information about the targets that is sufficiently rich to allow them not only to be tracked, but also identified as a person, a robot, etc. This identity information can be used to aid in man-machine interface design and to facilitate situation assessment. After defining the identification problem, a centralized Bayesian algorithm is developed for determining the identity of each target based on each sensor´s information. The algorithm is decentralized and its performance compared to the centralized version. Results of an implementation of the algorithm working on real data from the surveillance system are presented