Dinh‐Tan Pham
Papers
2
Total Citations
26
H-Index
2
About
Dinh-Tan Pham is a computer vision and machine learning researcher whose work centers on human action recognition, with a particular focus on skeleton-based approaches that leverage depth and skeletal data. His research addresses one of the field's most challenging problems: accurately identifying and classifying human movements for real-world applications including human-robot interaction, video surveillance, and gaming systems. Among his most notable contributions is his 2018 paper introducing a novel skeleton-based action recognition framework using covariance descriptors computed on the most informative joints — a targeted approach that improves recognition efficiency by prioritizing the body joints most relevant to distinguishing actions. This work has garnered 18 citations, reflecting its meaningful impact on the research community. Building on this foundation, his 2020 work on spatio-temporal representations for skeleton-based recognition further advanced the field by capturing both the spatial configuration and temporal dynamics of human motion, accumulating 8 citations. Pham's contributions are particularly valuable to researchers working at the intersection of computer vision, deep learning, and robotics, as his methods offer practical, discriminative solutions to human motion analysis. His body of work demonstrates a consistent commitment to improving the robustness and applicability of action recognition in diverse real-world scenarios.
Research Focus
Key Achievements
Top Papers
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