Abnormal Event Detection in Video Using N-cut Clustering

Abstract

This paper introduces an unusual event detection scheme in various video scenes. The proposed method finds out the video clips that are most different from the others based on the similarity measure. Each video clip is represented by the motion magnitude and direction histograms and color histogram. Without searching key-frames, we calculate the similarity matrix by using \chi^2 difference or chamfer difference as the similarity measure of features in different clips. Finally, we apply n-cut clustering. Clusters with low self-similarity value are reported as unusual events.