Intelligent Surveillance System Using Deep Learning to Reduce Shoplifting in Minimarkets in Santiago de Surco, Lima, Peru

Aguero Y.A.S.; Rengifo J.K.C. · Proceedings of the International Conference on Informatics in Control, Automation and Robotics (2025) · DOI: 10.5220/0013750800003982

Resumen

This article presents an intelligent video surveillance system for theft detection in minimarkets located in Santiago de Surco, Lima. The proposed solution integrates computer vision techniques with deep learning models such as Convolutional Neural Networks (CNN) and You Only Look Once (YOLO), implemented using PyTorch. The system analyzes customer movements in real time to detect suspicious behavior patterns, including torso twists and concealment attempts. Trained on a dataset of over 2700 real and simulated images, the model achieved an accuracy of 82%, outperforming traditional surveillance systems by more than 30%. The solution includes a web interface developed with FastAPI (Fast Application Programming Interface, a high-performance Python framework for building APIs) and Angular, enabling remote monitoring. Practically, the system can reduce economic losses by up to 15%, offering a scalable and cost-effective alternative for improving security in small commercial environments.

Instituciones: Universidad Peruana de Ciencias Aplicadas