Combination of Advanced Robotics and Computer Vision for Shelf Analytics in a Retail Store

Abstract

Large-scale retail store associates are constantly faced with the challenge of managing store operations smoothly and maintaining the products in full stock on the product support devices (retail shelves). Keeping track of the quantities of each individual Stock Keeping Unit (Retail Product), replenishing them when depleted, and identifying and replacing misplaced products are few tasks that require continuous monitoring and large amount of manual effort. The solution being presented here aims at automating the tasks performed by the store associates which result in reducing the manual effort. The solution proposes the use of a Double Robot to patrol the store over a fixed path and capture images of the retail shelves at real time. These images are processed using developed solution to address various retail store challenges such as stock out problems and misplaced products. An alert generating mechanism has also been incorporated into the solution to alert the store associate via email or a text message, when a product is completely out of stock/misplaced. The solution approaches use classification techniques, deep learning techniques along with computer vision algorithms to automate these processes in retail stores.