Xinqi Chu
Papers
1
Total Citations
7
H-Index
1
About
Xinqi Chu is a researcher whose work bridges computer vision and human-robot interaction (HRI), with a particular focus on enabling machines to understand and respond to human gestures. His key research areas include pose recognition, disparity imaging, and machine learning for interactive systems. Chu’s most cited paper, "Human Upper Body Pose Recognition Using Adaboost Template for Natural Human Robot Interaction" (2010), introduced a novel Adaboost template method that classifies standing persons’ upper body poses into seven distinct view categories. By leveraging disparity images and constructing mean and variance templates, his approach significantly improved the robustness of pose recognition in real-world HRI scenarios. This work has accumulated 7 citations, reflecting its foundational role in advancing natural, non-verbal communication between humans and robots. Chu’s contributions are notable for their practical emphasis on real-time, template-based recognition, offering a scalable solution for interactive systems. His research continues to inspire developments in assistive robotics and autonomous systems, where intuitive human-machine interaction is critical.
Research Focus
Key Achievements
Top Papers
- 1