Multi-target surveillance in dynamic environments: Sensing-system reconfiguration

The active surveillance of maneuvering targets with multiple dynamic sensors requires a planning strategy to dynamically select and position the groups of sensors for optimal performance. This Thesis presents such a generic sensor-planning strategy that can be used for the real-time reconfiguration of a multi-sensor system for multi-object dynamic environments. The environment may include multiple Objects-of-Interest (OoI) as well as static and/or mobile objects that are not of interest (i.e., obstacles) but may act as occlusions. There should be no restrictions imposed on the number of sensors or their mobility as well as no requirement about knowing the objects' trajectories ahead of time.; It is proposed herein to handle the sensor-planning problem using two complementary strategies: A coordination strategy to determine how many and specifically which sensors should be used at each demand instant in order to optimize the performance of the surveillance system over the span of several data-acquisition instants; and, a positioning strategy to determine the optimal pose of each sensor for any data-acquisition instant being serviced.; The coordination strategy is proposed to be accomplished in this Thesis through an agent-based system consisting of multiple sensor agents, a referee agent, and a judge agent. Each sensor agent tries to maximize its own performance over the span of the rolling horizon. Although not directly controlled by a centralized controller, the sensor agents must abide the external rules of the environment monitored by the referee agent and enforced by the judge agent. The rules are set to ensure that the collective behaviour of the sensor agents exhibits the desired system behaviour. The positioning strategy is accomplished by determining regions of a sensor's workspace that are both unoccluded and achievable given the sensor's current pose, motion capabilities, and remaining time to data acquisition. The acceptable area is, then, searched for an optimal sensor pose.; In order to demonstrate the generality of the proposed methodology, it has been implemented in active-sensing for object localization, facial recognition, and object recognition via Shape-From-Shading (SFS). The applications cover surveillance of both time-variant and invariant parameters. Furthermore, they demonstrate the advantages of surveillance systems that utilizes multiple mobile sensors coupled with an effective sensor-planning strategy over static or single-sensor systems. The improvements in surveillance performance are primarily due to (i) increased robustness of the system (i.e., its ability to cope with a priori unknown target trajectories and presence of obstacles), (ii) decreased uncertainty associated with estimating the target's pose through sensor fusion, and (iii) increased reliability through sensory fault tolerance.