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

TL;DR

This paper introduces a computer vision approach that models product recognition on store shelves as a sub-graph isomorphism problem, enabling automated verification of shelf layouts to improve efficiency.

Contribution

It presents a novel formulation of product recognition as a sub-graph isomorphism problem, combining local invariant features for accurate and automated shelf layout verification.

Findings

High recognition accuracy demonstrated

Effective auto-localization within store aisles

Significant reduction in manual verification effort

Abstract

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task 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 the aisle or store and improving recognition dramatically.