Atchariyachanvanich, Kanokwan

Preferred name

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School of Information Technology

kanokwan.at@kmitl.ac.th

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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.
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Exploring User Attitudes and Innovative System Design for Remote Lighting Control Systems in Thailand's Creative Industries
(2024-01-01)
Jeerasottikule, Thammanoon
;
Cooharojananone, Nagul
;
Atchariyachanvanich, Kanokwan
In this paper, the authors investigate the current state of the lighting design and control sector in Thailand's creative industry. The government aims to promote the creative industry as a key source of income, but there needs more skilled professionals in the industry. The authors have found that successful cases have used the Internet for remote controlling in the creative industry in other countries. Therefore, the paper will explore the potential of using technology to improve professional efficiency and assess the feasibility of implementing remote lighting control systems via the Internet with Thai lighting designers and lighting console operators. Data will be collected through qualitative in-depth interviews and quantitative surveys. The results have shown a promising acceptance rate of wireless devices for lighting control due to their mobility, flexibility, cost-effectiveness, and positive attitudes toward adopting Internet technology. The authors have also proposed a concept design for an internet-based control system tailored to Thai users, focusing on simplicity, ease of connection, and user-friendliness to accommodate those with limited network configuration knowledge. The proposed system aims to reduce professionals, save time, and increase convenience, leveraging Thailand's extensive wireless internet coverage. These systems could significantly benefit Thailand's creative industry by addressing the shortage of skilled professionals and improving efficiency.
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Comparative Foot Traffic Analysis During Normal Periods and Firework Events Using Wi-Fi Sensors
(2024-01-01)
Traganmaturapot, Peerada
;
Sonehara, Noboru
;
Hiruma, Nobuharu
;
Cooharojananone, Nagul
;
Kodate, Akihisa
Recent observations indicate an increase in the frequency of crowd crush incidents, highlighting the urgent need for effective mitigation strategies. Addressing this issue necessitates a comprehensive understanding of the factors influencing visitor decision-making to prevent adverse outcomes such as crowd crushes. This study introduces a Streamlit dashboard designed to visualize foot traffic data in the Sendagaya area and integrates contextual data from 10 key factors, including events and locations, points of interest (POIs), periods of time, online search activity, height of buildings, temperature and weather conditions, currency exchange rates, earthquakes, number of international flight arrivals, and hotel room rates. This integration facilitates comparative analysis of foot traffic patterns during standard periods versus periods coinciding with significant events, such as the 2023 Jingu Gaien Fireworks Festival, to assess their impact on congestion levels. Conducted exclusively in Sendagaya, the study utilized 8 Wi-Fi sensors throughout August 2023, encompassing 3 key stages: data collection, preprocessing, and dashboard development. The analysis revealed significant determinants-including events, points of interest, time periods, and online activity-that influence visitor foot traffic, while other factors exhibited no discernible impact. These findings have important implications for enhancing decision-making processes, preparedness measures, risk management strategies, and data-driven policymaking for sustainable tourism development.
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Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News: A Case Study of Health and Medical Articles on Thai Language Websites
(2024-01-01)
Atchariyachanvanich, Kanokwan
;
Saengkhunthod, Chotipong
;
Kerdnoonwong, Parischaya
;
Chanlekha, Hutchatai
;
Cooharojananone, Nagul
These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model’s prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.
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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.

Atchariyachanvanich, Kanokwan

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