The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. Verifying compliance of real shelves to the ideal layout, however, is a costly task currently routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within aisles of the store and improves recognition dramatically.

Product recognition in store shelves as a sub-graph isomorphism problem

Tonioni, Alessio;Di Stefano, Luigi
2017

Abstract

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. Verifying compliance of real shelves to the ideal layout, however, is a costly task currently routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within aisles of the store and improves recognition dramatically.
2017
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
682
693
Tonioni, Alessio; Di Stefano, Luigi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/619497
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