Automatic Identification of on Shelf Stock Availability in Retail Shops Using GCN

Abstract

In retail industry ensuring on shelf availability is crucial for customer satisfaction. This project introduces an innovative solution for automated on shelf stock monitoring by Graph Convolution Network (GCN) fundamental algorithm as framework. This particular system will combine computer vision techniques to capture real time shelf images and employees GCN to process the visual data efficiently. Through graph based representation of store shelves and products, the GCN algorithm will analyze the items and their availability status. This project includes in data collection, image preprocessing, object detection. The application of GCN is used to construct a dynamic graph that capture items. This algorithm’s ability to model complex dependencies within the shelf inventory facilitates accurate stock availability. Alerts are generated when low stock or out of stock situations are detected. Keywords—super pixel graph ,Graph convolutional network ,SVM , shelf image and notification alert