Library Seat Hogging Detection Using Hybrid Real and AI-Generated Data

Efficient management of library seating resources is a critical challenge in educational institutions, often hindered by “seat hogging” behaviors where users occupy spaces with personal belongings without actual occupancy. Traditional manual inspections are labor-intensive and inefficient. This paper proposes an automated seat occupancy detection system utilizing existing CCTV infrastructure and Computer Vision techniques. We employ YOLOv8, a state-of-the-art object detection model, to identify two key classes: persons and belongings. To address the challenge of limited real-world datasets for specific library environments, we introduce a data augmentation strategy using AI-generated synthetic data produced by a generative model (Gemini 2.5 Pro). A rule-based algorithm is integrated to analyze the spatiotemporal relationship between detected persons and belongings, enabling the system to distinguish between “occupied,” “vacant,” and “hogged” states effectively. Experimental results demonstrate that the proposed hybrid dataset approach enhances detection performance, providing a scalable and cost-effective solution for smart library management. Furthermore, a pilot system evaluation yielded an overall accuracy of 91.62%, validating the system’s effectiveness for real-world deployment.