Publications

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Journal Articles

Unsupervised Hand Tracking for Sign Language Recognition via Optical Flow and Previous Tracking Data

Published in IEEE Access, 2026

An unsupervised, model-free hand tracking framework for sign language recognition that uses dense optical flow and temporal consistency, requiring no training data.

Recommended citation: Jawas, N., & Tjahjadi, T. (2026). "Unsupervised Hand Tracking for Sign Language Recognition via Optical Flow and Previous Tracking Data." IEEE Access, 14, 30125–30142. https://doi.org/10.1109/ACCESS.2026.3667503
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Conference Papers

Revisiting ST-GCN Design Choices for Skeleton-Based Continuous Sign Language Recognition

Published in Brawijaya International Conference (BIC) 2026 (accepted), 2026

Accepted for presentation at the Brawijaya International Conference (BIC) 2026.

Recommended citation: Jawas, N. (2026). "Revisiting ST-GCN Design Choices for Skeleton-Based Continuous Sign Language Recognition." Brawijaya International Conference (BIC) 2026, Kuala Lumpur, Malaysia. Accepted.

A Combined Method of Data-Driven Structural Damage Classification by Using Acceleration Data

Published in Machine and Computing Technologies for Sustainable Development (MCT4SD 2025), 2026

A combined data-driven method for classifying structural damage from acceleration data.

Recommended citation: Adha, A., Kurniawan, M., Syarif, F., Wardana, I. N. K., Jawas, N., Harmiyati, & Laory, I. (2026). "A Combined Method of Data-Driven Structural Damage Classification by Using Acceleration Data." In B. Tafuri, S. Kumar, & A. Joshi (Eds.), Machine and Computing Technologies for Sustainable Development: Proceedings of MCT4SD 2025, Volume 3 (pp. 377–386). Lecture Notes in Networks and Systems. Springer, Cham. https://doi.org/10.1007/978-3-032-23716-3_35
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Theses

Automatic Sign Language Recognition Using Colour Image Representation of Dense Optical Flow and Multi-Cluster Optical Flow Map

Published in PhD thesis, School of Engineering, University of Warwick, 2024

PhD thesis on automatic sign language recognition from standard RGB video, covering a two-hand tracking algorithm and a recognition system built on a multi-cluster optical flow map.

Recommended citation: Jawas, N. (2024). "Automatic Sign Language Recognition Using Colour Image Representation of Dense Optical Flow and Multi-Cluster Optical Flow Map." PhD thesis, University of Warwick. https://wrap.warwick.ac.uk/id/eprint/190559/
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Naser Jawas