A synergistic model for interpreting human activities and events from video: a case study

This paper describes a new approach for representing, recognizing and interpreting human activity from video. The approach presented (at the conceptual level) is a model based on the hierarchical synergy of three other models (the L-G graph, the SPN graph and a NN model). In particular, in our project human activity is strongly related with the ability of describing and interrelating events. Thus, the L-G graph (local-global graph) provides a powerful description of the structural image features presented in an event, the SPN (stochastic Petri net) model offers a description of the functional behavior of the changes or operations in video presented in an event, and the NN (neural network) model provides the capability of extracting and learning behavioral patterns, presented in human activities.