A comparative study of video segmentation techniques for graduate detection in Thai graduation ceremonies

Name: Chanasorn Chungmarisakul

Organization : King Mongkut's Institute of Technology Ladkrabang. Department of Electrical and Computer Engineering, School of Engineering

Email : 67016018@kmitl.ac.th

Name: Rathachai Chawuthai

Organization : King Mongkut's Institute of Technology Ladkrabang. Department of Computer Engineering, School of Engineering

Email : rathachai.ch@kmitl.ac.th

keyword: Object detection (Computer vision)

LCSH: Image segmentation -- Data processing

LCSH: Deep learning (Machine learning)

LCSH: Video recording

LCSH: Computer vision -- Thailand

Abstract: 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.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2025

Modified: 2026-06-10

Issued: 2026-06-10

บทความ/Article

application/pdf

BibliograpyCitation : In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI) Association, Thailand. 2025 22nd International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2025) (pp.122-126). Piscataway, NJ : IEEE

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