Tien-Nam Nguyen
Papers
2
Total Citations
26
H-Index
2
About
Tien-Nam Nguyen is a researcher specializing in computer vision and human action recognition, with a particular focus on skeleton-based methods. His work addresses the challenge of enabling machines to understand and interpret human movements from skeletal data—a critical capability for applications in human-robot interaction, gaming, and video surveillance. Nguyen’s major contributions include the development of novel spatio-temporal representations and covariance descriptors that capture the most informative joints in human motion, improving the accuracy and efficiency of action recognition systems. His most cited paper, "Novel Skeleton-based Action Recognition Using Covariance Descriptors on Most Informative Joints" (2018), has garnered 18 citations, reflecting its impact on advancing skeletal data analysis. A subsequent work, "Spatio-Temporal Representation for Skeleton-based Human Action Recognition" (2020), with 8 citations, further refines these techniques by integrating temporal dynamics. Nguyen’s research stands out for its focus on lightweight, discriminative features that enhance real-time performance, making his contributions valuable for both academic researchers and practitioners developing interactive systems. His work continues to influence the growing field of human-centered computing.
Research Focus
Key Achievements
Top Papers
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- 2