Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause-eect relationships between user actions and changes in environment congurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an eective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledgetransfer phase, learned representations can be used, at dierent levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario, where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator. These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, Department of Naval, Electric, Electronic and Telecommunications Engineering, University of Genoa Via Opera Pia 11A, 16100, Genoa, Italy Tel.: +39-010-3532212 Fax: +39-010-3532134 e-mail: (see http://www.isip40.it) 2 Simone Chiappino et al. on synthetic and also on real video sequences, in both the user-support and automatic modes.
Bio-inspired relevant interaction modelling in Cognitive crowd management
CHIAPPINO, SIMONE;MORERIO, PIETRO;MARCENARO, LUCIO;REGAZZONI, CARLO
2014-01-01
Abstract
Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause-eect relationships between user actions and changes in environment congurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an eective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledgetransfer phase, learned representations can be used, at dierent levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario, where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator. These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, Department of Naval, Electric, Electronic and Telecommunications Engineering, University of Genoa Via Opera Pia 11A, 16100, Genoa, Italy Tel.: +39-010-3532212 Fax: +39-010-3532134 e-mail: (see http://www.isip40.it) 2 Simone Chiappino et al. on synthetic and also on real video sequences, in both the user-support and automatic modes.
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