Selective Attention Automatic Focus for cognitive crowd monitoring

In most recent Intelligent Video Surveillance systems, mechanisms used to support human decisions are integrated in cognitive artificial processes. Large scale video surveillance networks must be able to analyse a huge amount of information. In this context, a cognitive perception mechanism integrate in an intelligent system could help an operator for focusing his attention on relevant aspects of the environment ignoring other parts. This paper presents a bio-inspired algorithm called Selective Attention Automatic Focus (S2AF), as a part of more complex Cognitive Dynamic Surveillance System (CDSS) for crowd monitoring. The main objective of the proposed method is to extract relevant information needed for crowd monitoring directly from the environmental observations. Experimental results are provided by means of a 3D crowd simulator; they show how by the proposed attention focus method is able to detect densely populated areas.