Embedded Vision System for Real-Time Shelves Rows Detection for Planogram Compliance Check

Abstract

Abstract In retail environment monitor store shelves is a key factor for retailers and brands to provide the best customer shopping experience and maximize sales. Computer vision and deep learning are well suitable this task and are already used for detection and recognition of products displayed in shelves. Recently, retailers started using autonomous robotic applications for monitoring store shelves. Commercial robotic solutions for full store inventory are rising on the market, equipped with Radio Frequency Identification (RFID) technology or vision-based systems. Such robots usually browse the store fulfilling a task regarding inventory or planogram compliance check. Detect and recognize product on a shelf, however, is not enough to have a proper picture of a shelf. Physical structure of the shelf must be taken into account in order to assign every detected product to its shelf row. Know exactly in which shelf row a product is displayed enables the calculation of some specific Key Performance Indicators (KPIs) such as the Share of Shelf. In this paper after analyzing the techniques currently used in the state-of-the-art, we realized that there is no reliable and lightweight solution to detect shelf rows, so we provide an end-to-end solution for that and we prove the feasibility of the approach on a newly collected dataset.