Papers
1
Total Citations
9
H-Index
1
About
Chuping Lee is a robotics researcher specializing in autonomous mobile systems and human-robot interaction, with a particular focus on real-time perception and tracking. Their most cited work, "A real-time pedestrian legs detection and tracking system used for autonomous mobile robots" (2017, 9 citations), introduces a novel approach to detecting and following human movement by targeting the lower body—a practical solution for robots navigating crowded environments. In this study, Lee systematically evaluates two machine learning classifiers, multilayer perceptron (MLP) and support vector machines (SVM), to optimize detection accuracy and speed, demonstrating a strong commitment to bridging algorithmic efficiency with real-world robotic applications. This contribution is especially valuable for developing safer, more responsive autonomous robots in settings like hospitals, warehouses, or public spaces. While early in their career, Lee’s work lays a solid foundation for advancing robot perception in dynamic human-centric environments, and their focus on leg detection offers a unique perspective compared to full-body tracking approaches. As Lee continues to publish, their research promises to influence both robotics engineering and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1