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摘要翻译：
监视的一个核心问题是在大范围、障碍物和遮挡的环境中监视多个运动目标。本文提出了一种新的基于部分可观测马尔可夫决策过程的协调和控制主动摄像机网络的方法，用于在这样的监视环境中以高分辨率跟踪和观察多个移动目标。我们提出的方法能够(a)保持对目标状态（即位置、方向和速度）的信念来跟踪它们，即使它们可能不是一直被摄像机直接观察到；(b)协调摄像机的行动以同时提高对目标状态的信念，并在保证分辨率的情况下最大限度地增加预期的观察目标数量；(c)利用我们的监视问题的固有结构来提高其对待观察目标数量的可伸缩性（即线性时间）。通过与当前多摄像机协调控制技术的定量比较，表明该方法可以实现更高的实时监控质量。用实轴214云台摄像机验证了该方法的可行性
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英文标题：
《Decision-Theoretic Coordination and Control for Active Multi-Camera
Surveillance in Uncertain, Partially Observable Environments》
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作者：
Prabhu Natarajan, Trong Nghia Hoang, Kian Hsiang Low, Mohan
Kankanhalli
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science 计算机科学
二级分类：Multimedia 多媒体
分类描述：Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Robotics 机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
A central problem of surveillance is to monitor multiple targets moving in a large-scale, obstacle-ridden environment with occlusions. This paper presents a novel principled Partially Observable Markov Decision Process-based approach to coordinating and controlling a network of active cameras for tracking and observing multiple mobile targets at high resolution in such surveillance environments. Our proposed approach is capable of (a) maintaining a belief over the targets' states (i.e., locations, directions, and velocities) to track them, even when they may not be observed directly by the cameras at all times, (b) coordinating the cameras' actions to simultaneously improve the belief over the targets' states and maximize the expected number of targets observed with a guaranteed resolution, and (c) exploiting the inherent structure of our surveillance problem to improve its scalability (i.e., linear time) in the number of targets to be observed. Quantitative comparisons with state-of-the-art multi-camera coordination and control techniques show that our approach can achieve higher surveillance quality in real time. The practical feasibility of our approach is also demonstrated using real AXIS 214 PTZ cameras
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PDF链接：
https://arxiv.org/pdf/1209.4275
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