A Deep Learning Approach for Product Detection in Intelligent Retail Environment

Abstract

A planogram is the graphical representation of the way a given number of products are positioned within the shelves in a store. The creation of a correct planogram is a fundamental tool for a store’s performance: it helps to increase sales and achieve maximum customer satisfaction by reducing out-of-stocks. To this end, this work aims to provide an automatic object recognition based system that allows the operator to verify the correctness of a planogram. For image acquisition, either low-cost battery-powered cameras positioned on the opposite side of the shelf or simply a tablet with a dedicated app can be used. These tools are connected to the cloud where the detection and matching phases are performed. The experimental results come from a real environment.