Framework for Contextual Outlier Identification using Multivariate Analysis approach and Unsupervised Learning

Abstract

Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier. Using an analytical methodology, the proposed system blocking operation followed by sparsity to perform detection. The study outcome shows that proposed system offers an increasing level of accuracy in contrast to the existing system with faster response time.