SeSAME: Re-identification-based ambient intelligence system for museum environment

Marina Paolanti, Roberto Pierdicca, Rocco Pietrini, Massimo Martini, Emanuele Frontoni

Abstract: Highlights•SeSAME system for collecting and analysing the behaviours of visitors inside a museum environment.•SeSAME is based on a multi-modal deep neural network architecture.•SeSAME uses re-identification techniques to perform visual profiling of visitor interest.•SeSAME aims at developing a tailored user experience in museum environment.