Video Data Mining Framework for Surveillance Video
In this paper, the authors present a framework for surveillance videos of stationery places. To start with, they implement an algorithm to group incoming video stream into meaningful pieces called segments. Further they extract a feature of segment (i.e. motion) which is used to characterize the segments. Motion of a segment is extracted using two dimensional matrixes which are constructed using accumulated pixel differences among all frames in a segment. Video segments are then clustered using K-means algorithm. Then they find the abnormality in the segments of the video.