Cheng-Ming Huang

Papers

Total Citations

H-Index

About

Cheng-Ming Huang is a researcher whose work lies at the intersection of computer vision and human-robot interaction, with a particular focus on enabling machines to perceive and respond to human motion. His key research areas include visual tracking, human pose estimation, and real-time gesture recognition for robotic systems. Huang's most cited work, "Visual tracking of human head and arms with a single camera" (2010, 13 citations), addresses a fundamental challenge in human-robot interaction: tracking upper body movements using only a monocular camera. This is significant because it eliminates the need for expensive multi-camera setups or wearable sensors, making human-robot interaction more accessible and practical. The paper's core contribution is a robust tracking algorithm that operates in real time on moving camera platforms—a critical requirement for robots that navigate dynamic environments. By reducing the dimensionality of the human posture model, Huang's method achieves computational efficiency without sacrificing accuracy. This work has influenced subsequent research in markerless motion capture and interactive robotics, demonstrating how clever algorithmic design can bridge the gap between computer vision and autonomous systems.