Jingxin Cai
Papers
2
Total Citations
51
H-Index
2
About
Jingxin Cai is a robotics researcher whose work focuses on enabling mobile service robots to reliably follow humans in complex, real-world environments. Her key research areas include human-robot interaction, computer vision, and sensor fusion for autonomous navigation. Cai’s major contributions address the critical challenge of persistent target tracking, particularly when a person is temporarily lost from view. In her highly cited 2018 paper, "Laser-Based Intersection-Aware Human Following," she developed a novel method that uses laser range data to predict a person’s path around corners in indoor corridors, preventing permanent target loss due to occlusion—a problem that had long plagued robot following systems. Building on this, her 2019 work, "Fusing Skeleton Recognition With Face-TLD," introduced IFace-TLD, a robust multi-modal tracking system that combines face recognition with skeleton data to maintain a lock on a specific person even in crowded or visually cluttered scenes. With over 50 combined citations for these two foundational papers, Cai’s research has directly advanced the practical deployment of service robots in settings like hospitals, hotels, and homes, where reliable human following is essential for assistance and companionship.
Research Focus
Key Achievements
Top Papers
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- 2