Learning to recognize complex actions using conditional random fields

Surveillance systems that operate continuously generate large volumes of data. One such system is described here, continuously tracking and storing observations taken from multiple stereo systems. Automated event recognition is one way of annotating track databases for faster search and retrieval. Recognition of complex events in such data sets often requires context for successful disambiguation of apparently similar activities. Conditional random fields permit straightforward incorporation of temporal context into the event recognition task. This paper describes experiments in activity learning, using conditional random fields to learn and recognize composite events that are captured by the observation stream.