PTZ视觉传感器网络的合作环境监测：基于收益的学习方法
Cooperative Environmental Monitoring for PTZ Visual Sensor Networks: A Payoff-based Learning Approach
AI总结：
本文针对PTZ视觉传感器网络，提出了一种基于博弈论合作控制和收益学习的分布式环境监测方法，通过设计势博弈效用函数和收益学习算法，使传感器无需环境先验信息即可最大化全局目标函数。
摘要：
本文研究了Pan-Tilt-Zoom (PTZ) 视觉传感器网络的合作环境监测问题。首先，我们提出了最优环境监测问题的新颖形式化，其目标函数与环境的不确定状态相互交织。此外，由于视觉数据量大，每个传感器需要通过本地计算和通信来执行处理。为了解决这些问题，我们提出了一种基于博弈论合作控制和收益学习的分布式解决方案。在第一阶段，设计了一个效用函数，使得所得到的博弈构成一个势博弈，势函数等于群体目标函数，并且所设计的效用可以通过本地图像处理和通信来计算。然后，我们提出了一种基于收益的学习算法，使得传感器无需任何环境状态的先验信息即可达到全局目标函数最大化。最后，我们通过实验证明了所提方法的有效性。
英文摘要：
This paper investigates cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novel formulation of the optimal environmental monitoring problem, whose objective function is intertwined with the uncertain state of the environment. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. To address the issues, we present a distributed solution to the problem based on game theoretic cooperative control and payoff-based learning. At the first stage, a utility function is designed so that the resulting game constitutes a potential game with potential function equal to the group objective function, where the designed utility is shown to be computable through local image processing and communication. Then, we present a payoff-based learning algorithm so that the sensors are led to the global objective function maximizers without using any prior information on the environmental state. Finally, we run experiments to demonstrate the effectiveness of the present approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
