Hierarchical Event Representation and Recognition Method for Scalable Video Event Analysis

Abstract

Recognition of events in video is an important subject in intelligent video surveillance. In this paper, we propose a new paradigm of event recognition scheme from video. In this structure, most video events are represented by a hierarchical structure, efficient events representation and analysis of events are possible by using this property. We introduce a scalable and hierarchical event recognition method. First, events are classified into four hierarchical categories. Higher level events are organized by lower level events and relationships among them. We represent those relationships using temporal-logical constraints, that is, the event grammar, and a dynamic Bayesian network (DBN) combines the given event grammar with the probabilistic inference procedure to recognize an event. For scalability of the recognition system, all events in the hierarchy use the same framework of DBN. To recognize events efficiently in such a condition, we define the activation rate which is calculated by each event and propagated in bottom-up direction at each time step. We apply the proposed method to the experiments with a video segment simulating ticket office transactions.