Papers
6
Total Citations
274
H-Index
5
About
Jing Qi is a leading researcher in human-robot interaction (HRI), with a primary focus on computer vision-based hand gesture recognition. Her work addresses a critical challenge in robotics: enabling natural, intuitive communication between humans and machines. Qi’s most influential contribution is her comprehensive review on vision-based hand gesture recognition for HRI, which has garnered over 220 citations and serves as a foundational resource for the field. She has also developed innovative technical solutions, including the FGDSNet—a lightweight hand gesture recognition network designed for real-world robotic applications—and a novel CbCr-I component Gaussian mixture model for robust hand segmentation under variable lighting conditions. Beyond hand gestures, Qi has explored multimodal interaction, fusing hand postures with speech recognition for hexapod robots used in reconnaissance and rescue missions, and has advanced natural language processing for Chinese instruction-based robot control. Her work consistently bridges the gap between theoretical computer vision and practical robotic systems, achieving recognition for its impact on both academic research and real-world HRI applications.
Research Focus
Key Achievements
Top Papers
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- 33D Human Pose Estimation in Video for Human-Computer/Robot Interaction15 citations · 2023
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