Multiple feature set with feature selection for anomaly search in videos using hybrid classification

An examination of abnormal activities in video scenes is a very difficult task in computer vision community. An efficient anomaly detection technique to detect anomalies in crowded scenes is presented in this paper. It uses Multiple Feature Set (MFS) to represent a piece of rectangular region of predefined size in a video frame called as patch with Hybrid Classification (HC) using Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers for anomaly detection. The MFS contains a combination of the following types of features; gray intensity values, gradient edge features and texture energy map. The predominant features are selected from MFS by using a model of t-test feature selection method and are classified by HC model made up of GMM and SVM classifiers. The UCSD video clip database is used for performance analysis of MFS-HC system and compared with other approaches. Results show that MFS-HC provides better results than other approaches.