Acquisition of high-resolution images through online saccade sequence planning

This paper considers the problem of scheduling an active observer to visit as many targets in an area of surveillance as possible. We show how it is possible to plan a sequence of decisions regarding what target to look at through such a foveal-sensing action. We propose a framework in which a pan/tilt/zoom camera executes saccades in order to visit, and acquire high resolution images (at least one) of, as many moving targets as possible before they leave the scene. An intelligent choice of the order of sensing the targets can significantly reduce the total dead-time wasted by the active camera and, consequently, its cycle time. We cast the whole problem into a dynamic discrete optimization framework. In particular, we will show that the problem can be solved by modeling the attentional gaze control as a kinetic traveling salesperson problem whose solution is approximated by iteratively solving time dependent orienteering problems.Congestion analysis experiments are reported demonstrating the effectiveness of the solution in acquiring high resolution images of a large number of moving targets in a wide area. The evaluation was conducted with a simulation of a dual camera system in a master-slave configuration. We also report on preliminary experiments conducted using live cameras in a real surveillance environment.