Using Inactivity to Detect Unusual behavior
Using Inactivity to Detect Unusual behavior
复制标题
使用不活动来检测异常行为
DOI：
--
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Andrew Hunter
中科院分区：
文献类型：
--
作者：
Patrick Dickinson;Andrew Hunter
我们提出了一种新的方法来检测视频监控数据中的异常行为模式，适用于支持老年患者的家庭护理。我们的方法是基于检测不活动的异常模式。我们首先学习一个被观察场景的正常不活动空间图，用二维混合高斯函数表示。映射组件用于构建表示正常行为模式的隐马尔可夫模型。还推断了阈值模型，并通过比较模型可能性来检测异常行为。我们的学习过程是无监督的，并产生了一个高度透明的场景活动模型。我们对我们的方法进行了评估，并表明它可以有效地检测一系列参数设置中的异常行为。
We present a novel method for detecting unusual modes of behavior in video surveillance data, suitable for supporting home-based care of elderly patients. Our approach is based on detecting unusual patterns of inactivity. We first learn a spatial map of normal inactivity for an observed scene, expressed as a two-dimensional mixture of Gaussians. The map components are used to construct a Hidden Markov Model representing normal patterns of behavior. A threshold model is also inferred, and unusual behavior detected by comparing the model likelihoods. Our learning procedures are unsupervised, and yield a highly transparent model of scene activity. We present an evaluation of our approach, and show that it is effective in detecting unusual behavior across a range of parameter settings.