Abstract
In the context of retailing, the monitoring of consumer behaviours is particularly important for supporting vendors in their management and marketing decisions. Many studies have been carried out about various aspects and consequences of different behaviours. However, only recently the potential of computing systems is being used for automated data collection and processing. In this work, we present a novel pervasive system able to automatically monitor consumer behaviour in front of shelves in an intelligent retail environment (IRE). Data collected are stored into a cloud server for data analysis and insights, ready to be used by a Decision Support System (DSS).
The completely autonomous and low cost system proposed in this paper is based on a software infrastructure connected to a video sensor network. A set of computer vision algorithms, embedded in the distributed RGB-D cameras, provides information concerning customer behaviour, in particular, user-shelf interactions described with temporal and spatial features. This large number of analytics allows insight deductions. The use of distributed vision sensors inside a retail environment is novel and produces really valuable data for brands and retailers.
The feasibility and the effectiveness of the proposed architecture and approach have been tested on real retail environments.
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Liciotti, D., Frontoni, E., Mancini, A., Zingaretti, P. (2017). Pervasive System for Consumer Behaviour Analysis in Retail Environments. In: Nasrollahi, K., et al. Video Analytics. Face and Facial Expression Recognition and Audience Measurement. VAAM FFER 2016 2016. Lecture Notes in Computer Science(), vol 10165. Springer, Cham. https://doi.org/10.1007/978-3-319-56687-0_2
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DOI: https://doi.org/10.1007/978-3-319-56687-0_2
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