Audiovisual Gunshot Event Recognition

In this paper, we introduce a gunshot event recognition system based on audio and visual feature analysis. We model the gunshot event by a hierarchical probabilistic system. By incorporating gunshot sounds, human emotion and human activity analysis, we developed an effective semantic gunshot scene description from consumer video sequences. Moreover, our system also detects possible threatening scenes and wounded victim scenes which are closely related to real world gunshot scenes of violence. In addition to event modeling, we also employ optimized hierarchical audiovisual models in feature state detection to determine additional details including different types of guns, human emotions and human gesture of weapon discharge. Experimental results indicate the precision of gunshot event video content recognition is encouraging while the rate of false alarms is low. These favorable results arise from effectively capturing not only the event features themselves but also human responses inside the event. The effectiveness and flexibility of our system can benefit applications in the field of content-based video indexing, multimedia surveillance and human-computer interaction.