Scalable target coverage in smart camera networks

Smart camera networks are becoming increasingly popular in a number of application domains. In many applications, cameras are required to collaboratively track objects (e.g., habitat monitoring, or surveillance). In smart networks, camera coverage control is necessary to allow automatic tracking of targets without human intervention, allowing these systems to scale. In this paper, we consider the problem of automatic control of the cameras to maximize coverage of a set of targets. We formulate an optimization problem with the goal of maximizing the number of covered targets. Since the optimization problem is NP-hard, even for static targets, we propose a computationally efficient heuristic to reach near-optimal solution. Centralized solutions achieve excellent coverage, and can work well for small-scale networks, however they require significant communication cost for large scale networks. As a result, we propose an algorithm that spatially decomposes the network and computes optimal solutions for individual partitions. By decomposing the partitions in a way that minimizes dependencies between them, this approach results in coverage quality close to the centralized optimal solution, with an overhead and reaction time similar to those of distributed solutions.