Jerry Kah Eng Hoe
Papers
2
Total Citations
31
H-Index
2
About
Jerry Kah Eng Hoe is a robotics researcher whose work focuses on advancing human-robot interaction through robust computer vision and perception systems. His key research areas include object detection, pose recognition, and human tracking for service robots. Hoe’s major contributions lie in developing multi-stage and multi-model fusion algorithms that enhance the reliability and efficiency of robotic perception in real-world environments. His most-cited paper, “HOG based multi-stage object detection and pose recognition for service robot” (2010, 19 citations), introduces a three-stage algorithm that combines multi-class and bi-class HOG-based detectors to achieve low-cost object detection and pose recognition—a significant step for practical service robotics. Another notable work, “ML-fusion based multi-model human detection and tracking for robust human-robot interfaces” (2009, 12 citations), presents a stereo vision system integrating HOG-based detection, color tracking, and motion estimation for real-time human tracking on mobile robots. This fusion approach improves robustness in dynamic settings, directly impacting the design of safer, more intuitive human-robot interfaces. Hoe’s research demonstrates a clear commitment to bridging the gap between algorithmic innovation and deployable robotic systems, making his work a valuable reference for students and researchers in autonomous systems and computer vision.
Research Focus
Key Achievements
Top Papers
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