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

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.

[1]  Paul F. M. J. Verschure,et al.  A real-world rational agent: unifying old and new AI , 2003, Cogn. Sci..

[2]  J. Batlle,et al.  A behavior-based scheme using reinforcement learning for autonomous underwater vehicles , 2005, IEEE Journal of Oceanic Engineering.

[3]  F. Lavagetto,et al.  A Flexible Architecture for Ambient Intelligence Systems Supporting Adaptive Multimodal Interaction with Users , 2004 .

[4]  Tim J. Ellis,et al.  Learning semantic scene models from observing activity in visual surveillance , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  C. Regazzoni,et al.  Extraction of Aligned Video and Radio Information for Identity and Location Estimation in Surveillance Systems , 2004 .

[6]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[7]  Tim J. Ellis,et al.  Spatial and Probabilistic Modelling of Pedestrian Behaviour , 2002, BMVC.

[8]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Paul F. M. J. Verschure,et al.  An interactive space that learns to influence human behavior , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..

[11]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[12]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[13]  Héctor H. González-Baños,et al.  Motion strategies for maintaining visibility of a moving target , 1997, Proceedings of International Conference on Robotics and Automation.

[14]  A. Damasio.  The Feeling of What Happens: Body and Emotion in the Making of Consciousness , 1999 .