RFID-based solution for galleries and museums visit modelling using Markov model, BBN's and MAP decisions

RFID technologies are becoming increasingly popular and widely used in many applications. Tags can be used for environment and habit monitoring, healthcare applications, home automation and pedestrian or vehicle traffic control. This paper describes the method of building a robust N-state Markov model that describes visitor’s behaviour in a gallery room. The built model can be used in planning of exhibitions, in modelling of visitor’s preferences, and/or in generation of predictions related with exhibition lasting, expected sales and pricing. Presented system performance improvements are realised through Bayesian belief network (BBN) and maximum a posterior probability (MAP) decision approximation algorithm.