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- W2020329673 abstract "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." @default.
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- W2020329673 date "2014-11-04" @default.
- W2020329673 modified "2023-10-14" @default.
- W2020329673 title "Reinforcement Learning for Coverage Optimization Through PTZ Camera Alignment in Highly Dynamic Environments" @default.
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- W2020329673 doi "https://doi.org/10.1145/2659021.2659052" @default.
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