Learning and Sharing for Improved k-Coverage in Smart Camera Networks

In this paper we study the self-adaptive behaviour of smart camera networks. Each Camera is equipped with an adjustable zoom lens in order to improve the coverage redundancy formalised ask-coverage across all moving objects under two perspectives: i) learning the movement patterns of the objects captured by a reinforcement learning algorithm at an individual camera level, and ii) utilising a decentralised coordination strategy by enabling an inter-camera communication among the neighbours. Given the dynamic nature of the problem, the first contribution of the paper is to show how learning an environmental constraint such as the movement pattern of the objects leads to a dynamic zoom selection behaviour that significantly improves k-coverage across the network. In our second contribution we show that the speed of convergence of the learning approach can be improved by applying a knowledge-sharing scheme. This is achieved by employing an inter-camera communication strategy across the network. The results indicate that enabling a knowledge-sharing scheme retains the high performance of pure reinforcement learning approaches. It also leads to a considerably faster convergence to the maximum possible k-coverage in learning approaches across the majority of test scenarios.