Multi-Event Modeling and Recognition Using Extended Petri Nets

This paper addresses the event modeling and recognition problem in video surveillance systems using the system net on the Petri Net (PN) formalism. The single event on the foundation of prior knowledge is first modeled via arranging event schemes from the design document. Then, finite sequential runs (FSRs) determined by semantic features in training clips drive elements of the proposed single event model. Finally, the multi-event model is built automatically from single event models via a proposed integration method using multi-level features (including high-level semantic features and low-level features like numerical characters of individual trajectories). We provide a novel solution to event conflict issue through an extended system net where the resultant event type is determined by the decision tree technique. The comparison between the proposed methodology and other approaches in the literature is reported via experiments on an acknowledged public dataset.