Vision Sensor Scheduling for Multiple Target Tracking
This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem.
The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target.
The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.
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