Reinforcement Learning for Coverage Optimization Through PTZ Camera Alignment in Highly Dynamic Environments
Reinforcement Learning for Coverage Optimization Through PTZ Camera Alignment in Highly Dynamic Environments
复制标题
在高动态环境中通过 PTZ 摄像机对准进行强化学习以实现覆盖范围优化
DOI：
10.1145/2659021.2659052
复制
发表时间：
2014
影响因子：
--
通讯作者：
J. Hähner
中科院分区：
文献类型：
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作者：
Stefan Rudolph;Sarah Edenhofer;Sven Tomforde;J. Hähner
近年来，智能相机网络是一个深入研究的领域，因为此类系统的应用领域广泛。这项工作通过关注在运行时优化平移倾斜变焦（PTZ）摄像机网络的覆盖范围的问题，有助于实现此类应用。我们建议使用强化学习（RL）技术来解决这个问题。因此，我们首先介绍我们的底层模型及其 RL 上下文。然后，我们提出了相当新的 RL 算法 Distributed W-Learning，它专门用于多智能体系统。我们将该算法与当前的非学习、最先进的算法进行了比较。该论文展示了将强化学习应用于覆盖问题的潜在好处。尤其是在高动态环境中，PTZ 摄像机的性能可以显着提高。
In recent years, Smart Camera Networks are a field of intensive research, because of the versatile application areas of such systems. This work contributes to realize such applications by focusing on the problem of optimizing the coverage in a pan tilt zoom (PTZ) camera network during runtime. We propose to approach this problem with Reinforcement Learning (RL) techniques. Therefore, we first introduce our underlying model and its RL context. Then, we present the fairly new RL algorithm Distributed W-Learning, which is specialized for Multi Agent Systems. We compared the algorithm against current non-learning, state-of-the-art algorithms. The paper demonstrates the potential benefit of applying RL to coverage problems. Especially the performance of PTZ cameras in highly dynamic environments can be increased significantly.
在高动态优先观察区域中进行分布式三维相机对准
DOI：
10.1109/icdsc.2011.6042904
发表时间：
2011
期刊：
2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras
影响因子：
--
作者：
Jänen U;Grenz C;Hähner J.
通讯作者：
Hähner J.