Author(s): Pavithra M1 , Dr. M.Vimaladevi2 ME.,Phd, Priyanga M3 , Yamuna Devi S4

Theprevalent use of surveillance systems in road side, railwaystations, airports or malls has led to anenormous amountof data that wants to be analyzed for safety oreven commercial reasons.The mission of automatically detectingframes with anomalous or interesting events from longduration video sequences has concerned the research communityin the last decade. The existing system introduced a Swarm Intelligence based approach for Detecting InterestingEvents in Crowded Environments.The Histograms of Oriented Gradients (HOG) is used for capture the appearance information and Histograms of Oriented Swarms (HOS) is used for capture the frame dynamics. Both are combined to form a new descriptor that effectively characterizes each scene. However it does not considered dynamic texture to achieve high accuracy. To solve this problem the proposed system introduced histogram of Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) to represent dynamic texture.In a time window of each frame average triplets of HOG, HOS and LBP-TOP are consecutively computed. Then, these features are passed as an input to classifier. Here proximal support machine is used for classification. Proximal Support Vector Machine is based on Support Vector Machine, it is simpler and faster than traditional Support Vector Machines algorithm, which is especially suitable for large amounts of data or image classification and operations. The experimental results show that the proposed system achieves better performance compared with existing system.