An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity

Abstract

Improving the availability of products in a store in order to avoid the OOS (out-of-stock) problem is a crucial topic nowadays. The reduction of OOS events leads to a series of consequences, including, an increase in customer satisfaction and loyalty to the store and brand, the production of positive advertising with a consequent growth in sales, and finally an increase in profitability and sales for a specific category. In this context, we propose the Pallet Integrity system for the automatic and real-time detection of OOS on promo pallets and promo forecasting using computer vision. The system uses two cameras placed in top-view configuration; one equipped with a depth sensor used to determines the number of pieces on the pallet and the other, a very high resolution web-cam, that is used for the facing recognition. The computer vision depth process takes place on edge, while the product recognition and promo OOS alarms runs on the fog, with a processing unit per store; the multi-promo forecasting service and the data aggregation and visualization is on cloud. The system was extensively tested on different real stores worldwide with accurate OOS detection and forecasting results.