Discovery of Anomalous Event against Frequent Sequence of Video Events

Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos [1]. Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events (AEASP); that is to discover events which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector.