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.