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• 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. Nowadays, understanding and analysing visitors activities and behaviours is becoming imperative for personalising and improving the user experience in a museum environment. Users' behaviour can provide important statistics, insights and objective information about their interactions, such as attraction, attention and action. These data represent a precious value for the museum curators, and they are one of the parameters that need to be assessed. These information are collected through manual approaches based on questionnaires or visual observations. This procedure is time consuming and can be affected by the subjective interpretation of the evaluator. From such premises, SeSAME (Senseable Self Adapting Museum Environment) a novel system for collecting and analysing the behaviours of visitors inside a museum environment is presented in this paper. SeSAME is based on a multi-modal deep neural network architecture able to extract anthropometric and appearance features from RGB-D videos acquired in crowded environments. Our approach has been tested on four different temporal modelling methods to aggregate a sequence of image-level features into clip-level features. This paper uses as a benchmark TVPR2, a public dataset of acquired videos with an RGB-D camera in a top-view configuration, in the presence of persistent and temporarily heavy occlusion. Moreover, a dataset specifically collected for this work has been acquired in a real museum environment, which is Palazzo Buonaccorsi, an important historical building in Macerata, in Marche Region in the center of Italy. During the experimental phase, the evaluation metrics show the effectiveness and the suitability of the proposed method. [ABSTRACT FROM AUTHOR]

Coastal monitoring is strategic for the correct assessment of nearshore morphodynamics, to verify the effects of anthropogenic interventions for the purpose of coastal protection and for the rapid assessment of flooding vulnerability due to severe events. Remote sensing and field surveys are among the main approaches that have been developed to meet these necessities. Key parameters in the assessment and prevision of coastal flooding extensions, beside meteomarine characteristics, are the topography and slope of beaches, which can be extremely dynamic. The use of continuous monitoring through orthorectified video images allows for the rapid detection of the intertidal bathymetry and flooding threshold during severe events. The aim of this work was to present a comparison of different monitoring strategies and methodologies that have been integrated into repeated surveys in order to evaluate the performance of a new camera system. We used a low-cost camera based on Raspberry Pi called VISTAE (Video monitoring Intelligent STAtion for Environmental applications) for long-term remote observations and GNSS-laser tools for field measurements. The case study was a coastal tract in Riccione, Italy (Northern Adriatic Sea), which is the seat of nourishment interventions and of different types of underwater protection structures to combat coastal erosion. We performed data acquisition and analysis of the emerged beach and of the swash zone in terms of the intertidal bathymetry and shoreline. The results show a generally good agreement between the field and remote measurements through image processing, with a small discrepancy of the order of ≈0.05 m in the vertical and ≈1.5 m in the horizontal in terms of the root mean square error (RMSE). These values are comparable with that of current video monitoring instruments, but the VISTAE has the advantages of its low-cost, programmability and automatized analyses. This result, together with the possibility of continuous monitoring during daylight hours, supports the advantages of a combined approach in coastal flooding vulnerability assessment through integrated and complementary techniques. [ABSTRACT FROM AUTHOR]