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首页> 外文会议>IAAI-12;Innovative applications of artificial intelligence conference;AAAI conference on artificial intelligence;Symposium on educational advances in artificial intelligence;AAAI-12;EAAI-12 >Using POMDPs to Control an Accuracy-Processing Time Trade-Off in Video Surveillance
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Using POMDPs to Control an Accuracy-Processing Time Trade-Off in Video Surveillance

机译：在视频监视中使用POMDP控制精度处理时间的权衡

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摘要

With rapid profusion of video data, automated surveillance and intrusion detection is becoming closer to reality. In order to provide timely responses while limiting false alarms, an intrusion detection system must balance resources (e.g., time) and accuracy. In this paper, we show how such a system can be modeled with a partially observable Markov decision process (POMDP), representing possible computer vision filters and their costs in a way that is similar to human vision systems. The POMDP representation can be optimized to produce a dynamic sequence of operations and achieve a tradeoff between time and detection quality, taking into account uncertainty in the filter predictions. In a set of experiments on actual video data, we show that our method can both outperform static "expert" models and scale to large dynamic domains. These results suggest that our method could be used in real-world intrusion detection systems.
机译：随着视频数据的迅速普及，自动监视和入侵检测越来越接近现实。为了在限制误报的同时提供及时的响应，入侵检测系统必须平衡资源（例如时间）和准确性。在本文中，我们展示了如何使用部分可观察的马尔可夫决策过程（POMDP）对这种系统进行建模，以类似于人类视觉系统的方式表示可能的计算机视觉过滤器及其成本。考虑到滤波器预测的不确定性，可以优化POMDP表示以产生动态的操作序列，并在时间和检测质量之间进行权衡。在一组针对实际视频数据的实验中，我们证明了我们的方法不仅可以胜过静态“专家”模型，而且可以扩展到大型动态域。这些结果表明，我们的方法可用于现实世界的入侵检测系统。

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