A Method of Abnormal Action Recognition in Variable Scenarios
可变场所的异常行为识别方法

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.