Cooperative multi-target surveillance using a mutational analysis approach

Networked surveillance systems provide an extended perception and distributed sensing capability in monitored environments through the use of multiple networked sensors. The task of tracking multiple targets in a surveillance network is a challenging problem because of the following reasons: (1) multiple targets need to be monitored and tracked continuously so that they would not leave the view of at least one of the sensors; (2) the view of the sensors needs to be optimized so that at a given time the targets are observed with a discernable resolution for feature identification; (3) it is important to devise stable control algorithms for accomplishing the surveillance task. Current feature (point) based visual surveillance and tracking techniques generally employed do not provide an adequate framework to express a surveillance task. This paper presents a mutational analysis approach for shape based control to model a multi-target surveillance scenario. It further presents an optimal multiple sensor task planning algorithm based on the target resolution and priority, to achieve optimal coverage of multiple targets in the sensing region of the surveillance network. Finally, experimental results demonstrate the efficacy of the proposed approach for tracking multiple targets over a large area