Earticle

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

저자

• JungWoo Lee [ Department of Computer Engineering Kumoh National Institute of Technology Republic of Korea ]
• Youngwoo Kim [ Department of Computer Engineering Kumoh National Institute of Technology Republic of Korea ] Corresponding Author

참고문헌