Learning from experience is a basic task of human brain that is not yet fulfilled satisfactorily by computers. Therefore, in recent years to cope with this issue, bio-inspired approaches has gathered the attention of several researchers. In this work a learning method is proposed based on a model derived from neurophysiological observations of the generation of the sense of self which is connected to the memorization of the interaction with external entity. The domain of application where this algorithm is employed, is a Cognitive Surveillance system which aims at detecting intruders and communicate guidance messages to a user (a guard) provided with a mobile device in order to chase him. The proposed method intends to allow the system to establish an efficient interaction with the user by sending messages only when necessary. To this end the zones of the monitored area are classified according to the probability that a change in pursuit strategy will occur by learning online the motion of the user and the intruder. The proposed algorithm has been tested on real world data demonstrating the capacity of learning this information to be used to tag the zones of the area under exam.

"A Bio-inspired Learning Approach for the Classification of Risk Zones in a Smart Space"

REGAZZONI, CARLO;
2007

Abstract

Learning from experience is a basic task of human brain that is not yet fulfilled satisfactorily by computers. Therefore, in recent years to cope with this issue, bio-inspired approaches has gathered the attention of several researchers. In this work a learning method is proposed based on a model derived from neurophysiological observations of the generation of the sense of self which is connected to the memorization of the interaction with external entity. The domain of application where this algorithm is employed, is a Cognitive Surveillance system which aims at detecting intruders and communicate guidance messages to a user (a guard) provided with a mobile device in order to chase him. The proposed method intends to allow the system to establish an efficient interaction with the user by sending messages only when necessary. To this end the zones of the monitored area are classified according to the probability that a change in pursuit strategy will occur by learning online the motion of the user and the intruder. The proposed algorithm has been tested on real world data demonstrating the capacity of learning this information to be used to tag the zones of the area under exam.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/237643
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