在视觉分析中，人的同一动作在不同场景下会有截然不同的理解。为了判断在不同场景中行为是否为异常，在监控系统中使用双层词包模型来解决这个问题。把视频信息放在第1层包中，把场景动作文本词放在第2层包中。视频由一系列时空兴趣点组成的时空词典表示，动作性质由在指定场景下的动作文本词集合来确定。使用潜在语义分析概率模型(pLSA)不但能自动学习时空词的概率分布，找到与之对应的动作类别，也能在监督情况下学习在规定场景下运动文本词概率分布并区分出对应异常或正常行动结果。经过训练学习后，该算法可以识别新视频在相应场景下行为的异常或正常。

Different understanding results in different scenarios even for the same person to conduet visual analysis. In order to determine whether the behavior is abnormal in different scenarios, a double-layer bag-of-words model is proposed to solve the problem in surveillance system. The video information is processed in the first layer of Bag-of-Words, and the information of scenario-action text words is included in the second one. A video sequence is represented as a collection of spatial-temporal codebook by extracting space-time interest points. A behavior characteristic is represented as a collection of behavior text words in special scenarios. Probabilistic latent semantic analysis(pLSA)model is adopted to automatically learn the probability distributions of spatial-temporal words and the topics correspond to human action categories. PLSA also can learn the probability distributions of the motion text words in a scenario with supervisor and the topics correspond to anomalous or normal actions. The algorithm can categorize the human anomalous or normal action contained in the special occasion to a novel video sequence after being trained.