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.
CITATION STYLE
Tonioni, A., & Di Stefano, L. (2017). Product recognition in store shelves as a sub-graph isomorphism problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10484 LNCS, pp. 682–693). Springer Verlag. https://doi.org/10.1007/978-3-319-68560-1_61
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