Video event classification and anomaly identification using spectral clustering

This paper proposes a spectral clustering based methodology to classify video events and to detect anomalies. Feature trajectories from objects in a video are modelled, compared and clustered in order to classify the detected object events. Principles of normalized spectral clustering are used with modifications to affinity structure. A novel method for determining spectral clustering parameters based on Eigen structure of the affinity matrix is introduced. Employment of unsupervised learning for event classification is made possible by the proposed successive cluster identity labelling algorithm. A mechanism to identify abnormal events under the context is also introduced. The effectiveness and the robustness of the proposed methodology are demonstrated through experiments conducted on video streams focusing on human motion patterns.