Duc-Tuan Chu

Papers

Total Citations

H-Index

About

Duc-Tuan Chu is a researcher whose work sits at the intersection of computer vision, deep learning, and real-time tracking systems. His most cited paper, "An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter" (2023), addresses a critical challenge in automated surveillance and robotics: maintaining accurate, real-time human tracking. By integrating YOLOv4-tiny with the classic Camshift algorithm and a Kalman filter, Chu’s approach significantly enhances tracking robustness in dynamic environments, reducing drift and occlusion errors. This contribution is particularly valuable for applications in security, traffic control, and autonomous systems, where reliability under real-world conditions is paramount. With 6 citations since its publication, the work demonstrates early impact and relevance in a rapidly evolving field. Chu’s research exemplifies a practical, hybrid methodology that bridges traditional computer vision techniques with modern deep learning, offering a scalable solution for real-time tracking. His work continues to influence developments in intelligent surveillance and human-robot interaction, marking him as a promising contributor to the advancement of automated visual systems.