Web-Based Crowd Detection and Emotion Analysis for Fashion Retail Using Computer Vision

Valencia Rivera F.; Romero Polli E.; Bautista Ubillus E. · International Conference on Web Information Systems and Technologies, WEBIST - Proceedings (2025) · DOI: 10.5220/0013711300003985

Resumen

This study proposes a web-based solution to address the difficulty fashion retail stores face in obtaining accurate information on crowding and their customers' emotional states. Using computer vision techniques, the application leverages the YOLO algorithm for people detection and convolutional neural networks (CNN) for emotion classification. Integrating this data provides retailers with strategic insights to optimize space layout, improve resource allocation, and adjust their marketing strategies, allowing managers to make decisions based on objective data. The study emphasizes ethical considerations, including data anonymization and secure storage, and highlights limitations and future research directions, such as real-world testing and collaboration with retailers for contextually accurate data collection. The system was validated in a simulated environment that replicated the operating conditions of a retail store, allowing an initial evaluation of its performance. Copyright

Instituciones: Universidad Peruana de Ciencias Aplicadas