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

Arezoo Vejdanparast, Peter R. Lewis
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Abstract

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.
改进智能摄像机网络k-覆盖率的学习与共享
本文研究了智能摄像机网络的自适应行为。每个摄像头都配备了一个可调节的变焦镜头,以便在两个角度下提高覆盖冗余形式化的任务覆盖,覆盖所有移动物体:i)在单个摄像头级别学习强化学习算法捕获的物体的运动模式,以及ii)通过启用邻居之间的摄像头间通信来利用分散的协调策略。考虑到问题的动态性质,本文的第一个贡献是展示了学习环境约束(如物体的运动模式)如何导致动态缩放选择行为,从而显着提高整个网络的k覆盖率。在我们的第二个贡献中,我们展示了通过应用知识共享方案可以提高学习方法的收敛速度。这是通过采用跨网络的摄像机间通信策略来实现的。结果表明,启用知识共享方案保留了纯强化学习方法的高性能。它还导致在大多数测试场景中学习方法更快地收敛到最大可能的k覆盖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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