Papers
4
Total Citations
65
H-Index
4
About
Linhui Li is a researcher whose work sits at the intersection of computer vision, autonomous systems, and intelligent robotics. Her research focuses on enabling machines to perceive, predict, and interact with dynamic environments, with a particular emphasis on pedestrian safety and industrial automation. Li’s most impactful contribution is her 2019 paper on SVM-based image partitioning for AGV guide path recognition under complex illumination, which has garnered 36 citations and addresses a critical challenge for autonomous guided vehicles operating in real-world conditions. She further advanced the field with her 2022 work on causal temporal-spatial pedestrian trajectory prediction (CTSGI), which uses self-attention mechanisms to model contextual interactions and goal point estimation—a key innovation for autonomous vehicle safety in crowded spaces. Li has also contributed to monocular vision-based pedestrian detection and tracking, and more recently, to industrial robotics with a method for detecting key points on chemical barrel valves using Keypoint R-CNN and MobileNetV3. Her work bridges theoretical advances in trajectory forecasting with practical deployment in both autonomous driving and manufacturing, making her research essential reading for those working at the frontier of vision-based robotics.
Research Focus
Key Achievements
Top Papers
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- 3Study on pedestrian detection and tracking with monocular vision8 citations · 2010
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