ICNGC 2025 The 11th International Conference on Next Generation Computing 2025 (2025.12)바로가기
페이지
pp.145-148
저자
JungWoo Lee, Youngwoo Kim
언어
영어(ENG)
URL
https://www.earticle.net/Article/A478481
원문정보
초록
영어
Image-based sign language recognition frequently exhibits high computational costs and is susceptible to background noise. In this study, we introduce an efficient skeleton-based framework that utilizes 3D landmarks and transformers. A pivotal component of this approach is the creation of similar landmarks method, an ensemble technique that extracts and averages landmarks from multiple augmented video views. This approach enhanced the system's resilience to noise and missing coordinates. The model was evaluated using quadruple crossvalidation on a 30-word dataset combining AI-Hub (studio) and KSL (wild) data. The results demonstrated that the technique applying ensembles produced superior results in comparison to the technique not applied.
목차
Abstract I. INTRODUCTION II. METHODS A. Architrcture B. Dataset C. Video Augmentation D. Landmark Estimation E. Landmark Ensemble Process and Data Preprocessing F. Transformer-Based Classification Model III. RESULTS IV. DISUSSION V. CONCLUSION ACKNOWLEDGMENT REFERENCES