Event Detection and Clustering for Surveillance Video Summarization

Abstract

The target of surveillance summarization is to identify high-value information events in a video stream and to present it to a user. In this paper we present surveillance summarization approach using detection and clustering of important events. Assuming that events are main source of energy change between consecutive frames set of interesting frames is extracted and then clustered. Based on the structure of clusters two types of summaries are created static and dynamic. Static summary is build of key frames that are organized in clusters. Dynamic summary is created from short video segments representing each cluster and is used to lead user to the event of interest captures in key frames. We describe our approach and present experimental results.