Baokui Li

Papers

Total Citations

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About

Baokui Li is a researcher focused on advancing human-robot interaction and industrial automation through computer vision and machine learning. His work centers on developing intuitive, vision-based systems that enable robots to understand and respond to human gestures and efficiently handle industrial tasks. Li’s most-cited paper, “Static Hand Gesture Recognition for Human Robot Interaction” (2019), with 6 citations, proposes methods for robots to interpret hand signals, enhancing natural communication in collaborative environments. His second notable contribution, “A Fast Quantity and Position Detection Method Based on Monocular Vision for a Workpieces Counting and Sorting System” (2019), with 2 citations, addresses practical challenges in industrial sorting by introducing a rapid, monocular vision approach for detecting workpiece quantity and position. This work offers a cost-effective solution to improve efficiency in automated counting and sorting systems. Li’s research bridges the gap between theoretical vision techniques and real-world robotic applications, contributing to safer, more efficient human-robot collaboration and industrial automation. His efforts are particularly relevant for students and researchers exploring gesture-based interfaces and vision-driven industrial robotics.