Shop Guard AI : Shoplifting Detection using machine learning

Abstract

Shoplifting is a significant issue in the retail industry, leading to substantial financial losses, increased security costs, and disruptions to store operations. Traditional methods of monitoring through video surveillance are often insufficient, especially in large stores, due to the high volume of footage and the limited capacity of staff to monitor it in real time. This creates security vulnerabilities and increases the risk of theft. To address these challenges, we propose a machine learning-based solution designed to enhance theft detection in retail environments. Our approach leverages behavioral analysis and video segmentation to automatically process and analyze surveillance footage. By using advanced techniques such as neural networks, feature extraction, and anomaly detection, the system identifies suspicious activities in real time, enabling store personnel to take immediate action. This automated system reduces reliance on manual monitoring, improves security efficiency, and offers a scalable solution suitable for stores of all sizes. The implementation of these machine learning-based techniques aims to significantly mitigate the financial and operational impact of shoplifting in the retail industry.