Cong-Le Hieu
Papers
1
Total Citations
11
H-Index
1
About
Cong-Le Hieu is a researcher focused on advancing computer vision and intelligent surveillance systems, with a particular emphasis on efficient action recognition. His most cited work, "Action Recognition Based on Sequential 2D-CNN for Surveillance Systems" (2018), tackles the critical challenge of balancing computational speed with accuracy in real-world environments. By proposing a sequential 2D-CNN architecture, Hieu addresses the difficulties posed by variations in shape, illumination, and action complexity, offering a practical solution for time-sensitive applications like robot-human interaction and autonomous systems. This paper, with 11 citations, highlights his contribution to making deep learning models more deployable in resource-constrained settings. Hieu’s research sits at the intersection of efficiency and robustness, aiming to reduce processing time without sacrificing precision—a persistent hurdle in surveillance technology. His work is particularly relevant for students and engineers seeking to understand how to optimize neural networks for real-time video analysis, demonstrating a clear pathway from theoretical model design to applied system integration.
Research Focus
Key Achievements
Top Papers
- 1Action Recognition Based on Sequential 2D-CNN for Surveillance Systems11 citations · 2018