Chawuthai, Rathachai

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Chawuthai, Rathachai
Alternative Name
Chawuthai, R.
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Email
rathachai.ch@kmitl.ac.th

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    Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms
    (2023-07-01)
    Sharma, Nabin
    ;
    Baral, Sushish
    ;
    Paing, May Phu
    ;
    Chawuthai, Rathachai
    The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively.
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    U-GMo: Individual Clip Detection from a Graduation Ceremony Video
    (2024-01-01)
    Treesoonrat, Natee
    ;
    Kriengchaiyaprug, Nunnapat
    ;
    Upadhayawong, Thanakann
    ;
    Lohapongpan, Warinya
    ;
    Chawuthai, Rathachai
    Graduation ceremonies are important occasions in life. A video in this event is usually very long due to a lot of graduates getting their degree. This study suggests a method for automatically cutting the entire ceremony video into customized segments that only include the most significant events for each particular graduate, named U-GMo (Your Great Moment). The system uses deep learning with computer vision techniques, such as YOLOv8 for posture detection, to identify graduates by observing their motions and posture during the degree ceremony. After that, the identified bits are taken out and assembled into brief video snippets for every graduate. The algorithm can detect and extract each graduate's crucial moments with high performance, according to an examination conducted on a dataset of graduation ceremonies. The personalized video clips provide a convenient way to preserve the meaningful highlights from these milestone events.
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    Item type:Publication,
    A Comparative Study of Video Segmentation Techniques for Graduate Detection in Thai Graduation Ceremonies
    (2025-01-01)
    Chungmarisakul, Chanasorn
    ;
    Chawuthai, Rathachai
    Graduation ceremonies are significant occasions usually documented on lengthy, difficult-to-navigate videos. To make more satisfaction of the video needs to remove other participants and restore clear areas into short segments focused on particular graduates. By using YOLOv8 for efficient participant segmentation and the Segment Anything Model (SAM2) for accurate tracking, this study expands on earlier research by preparing videos for inpainting. The results establish the foundation for a complete system that combines inpainting, segmentation, and detection to produce polished, customized graduation video clips.