Shuying Cheng
Papers
1
Total Citations
8
H-Index
1
About
Shuying Cheng is a leading researcher in mobile robotics and human-robot interaction, with a core focus on developing robust, real-time human-following systems. Her most cited work, "An Efficient Human-Following Method by Fusing Kernelized Correlation Filter and Depth Information for Mobile Robot" (2019, 8 citations), addresses a critical challenge in assistive robotics: maintaining reliable tracking when traditional visual trackers fail. By fusing the fast, high-precision Kernelized Correlation Filter (KCF) with depth information, Cheng’s method significantly reduces drift—a common failure mode where the tracker loses its target due to occlusions or cluttered backgrounds. This contribution is pivotal for enabling mobile robots to cooperate intelligently and safely with humans in dynamic environments, from warehouses to healthcare settings. Her work exemplifies a practical, computationally efficient approach to human-aware navigation, bridging the gap between high-performance tracking algorithms and real-world deployment. Cheng’s research continues to influence the design of responsive, human-centric robotic systems, making her a key figure in advancing autonomous robot cooperation.
Research Focus
Key Achievements
Top Papers
- 1