Contextual Anomaly Detection Based Video Surveillance System

In this paper, a contextual anomaly event detection method is presented using a new clips boundaries detection approach and Bayesian classifier. Fall event is considered as anomaly event in our experiments and reported as case study. The anomaly score at frame levels is obtained. The proposed method involves three main phases; preprocessing for video content preparing, clips boundaries detection for anomaly behavior classification and fall event detection. The anomaly behavior - based fall event detection is classified into three main types; sudden change, gradual change and normal change within video sequence. To this end, a Bayesian classifier is trained to predict the anomaly score of video clips using similarity score prediction and acceleration raw data of sensors. We state quantitative results for clips boundaries detection, anomaly score prediction, and fall event detection rate. Further, the performance of the proposed anomaly event detection is evaluated based on results of common performance metrics (precision, sensitivity, specificity and accuracy) on public fall event datasets. The performance evaluation demonstrates a superiority of fall detection rate compared with recent researches in term of frame-level.