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Sensors (Basel). 2015 Aug 27;15(9):21114-33. doi: 10.3390/s150921114.

Embedded Vision Sensor Network for Planogram Maintenance in Retail Environments.

Author information

1
Dipartimento di Ingegneria dell'Informazione (DII), Università Politecnica delle Marche,Via Brecce Bianche, Ancona I-60131, Italy. frontoni@dii.univpm.it.
2
Dipartimento di Ingegneria dell'Informazione (DII), Università Politecnica delle Marche,Via Brecce Bianche, Ancona I-60131, Italy. mancini@dii.univpm.it.
3
Dipartimento di Ingegneria dell'Informazione (DII), Università Politecnica delle Marche,Via Brecce Bianche, Ancona I-60131, Italy. zinga@dii.univpm.it.

Abstract

A planogram is a detailed visual map that establishes the position of the products in a retail store. It is designed to supply the best location of a product for suppliers to support an innovative merchandising approach, to increase sales and profits and to better manage the shelves. Deviating from the planogram defeats the purpose of any of these goals, and maintaining the integrity of the planogram becomes a fundamental aspect in retail operations. We propose an embedded system, mainly based on a smart camera, able to detect and to investigate the most important parameters in a retail store by identifying the differences with respect to an "approved" planogram. We propose a new solution that allows concentrating all the surveys and the useful measures on a limited number of devices in communication among them. These devices are simple, low cost and ready for immediate installation, providing an affordable and scalable solution to the problem of planogram maintenance. Moreover, over an Internet of Things (IoT) cloud-based architecture, the system supplies many additional data that are not concerning the planogram, e.g., out-of-shelf events, promptly notified through SMS and/or mail. The application of this project allows the realization of highly integrated systems, which are economical, complete and easy to use for a large number of users. Experimental results have proven that the system can efficiently calculate the deviation from a normal situation by comparing the base planogram image with the images grabbed.

KEYWORDS:

computer vision; embedded sensors; planogram integrity; retail environments; wireless sensor networks

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