Scenario-Based Video Event Recognition by Constraint Flow

Abstract

We present a novel approach to representing and recognizing composite video events. A composite event is specified by a scenario, which is based on primitive events and their temporal-logical relations, to constrain the arrangements of the primitive events in the composite event. We propose a new scenario description method to represent composite events fluently and efficiently. A composite event is recognized by a constrained optimization algorithm whose constraints are defined by the scenario. The dynamic configuration of the scenario constraints is represented with constraint flow, which is generated from scenario automatically by our scenario parsing algorithm. The constraint flow reduces the search space dramatically, alleviates the effect of preprocessing errors, and guarantees the globally optimal solution for recognition. We validate our method to describe scenario and construct constraint flow for real videos and illustrate the effectiveness of our composite event recognition algorithm for natural video events.