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An augmented reality application for improving shopping experience in large retail stores

  • Edmanuel Cruz
  • Sergio Orts-Escolano
  • Francisco Gomez-Donoso
  • Carlos Rizo
  • Jose Carlos Rangel
  • Higinio Mora
  • Miguel Cazorla
S.I. : Virtual Reality, Augmented Reality and Commerce
  • 910 Downloads

Abstract

In several large retail stores, such as malls, sport or food stores, the customer often feels lost due to the difficulty in finding a product. Although these large stores usually have visual signs to guide customers toward specific products, sometimes these signs are also hard to find and are not updated. In this paper, we propose a system that jointly combines deep learning and augmented reality techniques to provide the customer with useful information. First, the proposed system learns the visual appearance of different areas in the store using a deep learning architecture. Then, customers can use their mobile devices to take a picture of the area where they are located within the store. Uploading this image to the system trained for image classification, we are able to identify the area where the customer is located. Then, using this information and novel augmented reality techniques, we provide information about the area where the customer is located: route to another area where a product is available, 3D product visualization, user location, analytics, etc. The system developed is able to successfully locate a user in an example store with 98% accuracy. The combination of deep learning systems together with augmented reality techniques shows promising results toward improving user experience in retail/commerce applications: branding, advance visualization, personalization, enhanced customer experience, etc.

Keywords

Smart shopping Deep learning Augmented reality Retail stores User experience Human–computer interaction 3D visualization 

Notes

Acknowledgements

This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds. It has also been supported by the University of Alicante Project GRE16-19.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto Universitario de Investigación InformáticaUniversidad de AlicanteAlicanteSpain

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