Title :
Selective attention automatic focus for cognitive crowd monitoring
Author :
Chiappino, Simone ; Marcenaro, Lucio ; Regazzoni, Carlo
Author_Institution :
DITEN, Univ. of Genoa, Genoa, Italy
Abstract :
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.
Keywords :
video surveillance; 3D crowd simulator; CDSS; S2AF; attention focus method; cognitive artificial processes; cognitive crowd monitoring; cognitive dynamic surveillance system; cognitive perception mechanism; densely populated area detection; intelligent video surveillance systems; selective attention automatic focus; Algorithm design and analysis; Cost function; Data mining; Entropy; Surveillance; Trajectory;
Conference_Titel :
Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference on
Conference_Location :
Krakow
DOI :
10.1109/AVSS.2013.6636609