Duong-Hung Hoang

Papers

Total Citations

H-Index

About

Duong-Hung Hoang is a researcher whose work lies at the intersection of computer vision and intelligent surveillance systems, with a particular focus on action recognition. His most-cited paper, "Action Recognition Based on Sequential 2D-CNN for Surveillance Systems" (2018, 11 citations), addresses a critical challenge in the field: balancing computational efficiency with high accuracy in real-world environments. Hoang’s contribution is notable for tackling the inherent difficulties of action recognition—such as variations in human shape, changing illumination, and the complexity of actions—while emphasizing the practical need for speed and precision in surveillance, human-robot interaction, and autonomous systems. By proposing a sequential 2D-CNN architecture, he offered a more lightweight alternative to computationally expensive 3D models, making real-time deployment more feasible. Though his citation count reflects a focused, emerging impact, his work is particularly relevant for researchers and engineers developing cost-effective, robust monitoring solutions. Hoang’s research underscores a pragmatic approach to deep learning, prioritizing deployability without sacrificing performance, and positions him as a contributor to the ongoing evolution of intelligent, responsive surveillance technologies.