Yinbei Li
Papers
1
Total Citations
2
H-Index
1
About
Yinbei Li’s research lies at the intersection of human-robot interaction, computer vision, and mobile robotics, with a particular focus on enabling robots to track and follow humans reliably in real-world environments. Their most-cited work, “A Skeleton and Visual Tracking Fusion Based Person-Following System for Mobile Service Robots” (2020), addresses a critical challenge in service robotics: maintaining robust person-following despite skeleton loss or body occlusion. Li proposed a novel fusion method that integrates a human skeleton tracker with a discriminative correlation filter enhanced by channel and spatial reliability, achieving real-time, robust tracking. This contribution is foundational for applications ranging from assistive robots to autonomous delivery systems, where consistent human-robot proximity is essential. While early in their career, Li’s work has already garnered attention, with the paper cited twice and serving as a reference point for researchers tackling occlusion and tracking failures in dynamic environments. Li’s approach exemplifies a practical, systems-level mindset—combining deep learning-based skeleton detection with classical visual tracking to create a solution that is both computationally efficient and resilient. For students and researchers entering the field of mobile robotics, Li’s work offers a clear blueprint for fusing multiple sensing modalities to achieve reliable, real-world performance.
Research Focus
Key Achievements
Top Papers
- 1