• G. Sudheer
• B. Jaswanth Singh
• B. Divya Sri
• A. Ganesh

In this paper, Monitoring and surveying are increasingly used in retail shops, since they improve the overall performance of the warehouses and reduce the manpower by using this project. The aim is to develop an algorithm for early detection of out-of-stock situations with regard to perishable goods stored in countertop shelves, refrigerated counters, baskets or crates. By using Surveillance camera to automatically detect Shelf Out of Stock (SOOS) based on Convolutional Neural Networks (CNNs). To ensure high on-shelf availability, which is a key factor for improving profits in retail stores. Our method first detects empty spaces in the shelves. It then classifies the detected change regions into several classes representing the actual changes on the shelves, such as “Non empty (full of stock)” and “empty(out of stock)”, by supervised learning using convolutional neural networks. It finally updates the shelf condition representing the presence/absence of products using classification results and computes. The output of the system can be used to generate alerts for store managers where shelf out of stock occur, as well as to continuously update empty and non-empty shelves for automated stock ordering and replenishment.

CNN(convolution neural network),background substraction,Hungarian method

"Automated On-shelf Stock Availability By Using CNN Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.g254-g258, March-2023, Available :http://www.jetir.org/papers/JETIR2303638.pdf

"Automated On-shelf Stock Availability By Using CNN Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppg254-g258, March-2023, Available at : http://www.jetir.org/papers/JETIR2303638.pdf