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Author: search.filters.author.Atchariyachanvanich, KanokwanSubject: search.filters.subject.CCTV

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    Library Seat Hogging Detection Using Hybrid Real and AI-Generated Data
    (2026-01-01)
    Viwatanawatanakarn, Natchanon
    ;
    Cooharojananone, Nagul
    ;
    Muangsin, Veera
    ;
    Tea-Makorn, Pin Pin
    ;
    Atchariyachanvanich, Kanokwan
    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.
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    BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV
    (2024-01-01)
    Kujareanpaisal, Poonnaphop
    ;
    Mayhasap, Rujira
    ;
    Tea-Makorn, Pin Pin
    ;
    Jindahra, Pavitra
    ;
    Starita, Stefano
    The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.