Joo Kooi Tan
Papers
10
Total Citations
74
H-Index
5
About
Joo Kooi Tan is a computer vision and robotics researcher whose work spans human motion recognition, visual tracking, and human-robot interaction. Over more than two decades of scholarly contribution, Tan has developed foundational techniques for understanding and interpreting human movement from video data. His early work on Motion History Images and eigenspace methods established efficient pipelines for real-time motion recognition, while subsequent research introduced directional motion history and energy images to tackle the complexities of multi-action classification. His contributions to motion segmentation — breaking complex activities into recognizable action primitives — have been particularly influential, earning his 2009 paper on temporal motion recognition 16 citations. Tan has also advanced robust visual tracking methodologies, including color histogram-based object tracking under unstable illumination and particle filter-driven face tracking. His work bridges perception and autonomy, extending to aerial robots capable of tracking and recognizing human motion in surveillance contexts. More recently, Tan has directed his expertise toward human-robot cooperation frameworks and deep learning-based object detection for assistive robotics, reflecting a consistent commitment to developing intelligent systems that meaningfully interact with and support people in everyday environments.
Research Focus
Key Achievements
Top Papers
- 1Temporal motion recognition and segmentation approach16 citations · 2009
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- 5Development of an autonomous robot for face tracking6 citations · 2007
- 6Real-time human motion recognition by an aerial robot5 citations · 2005
- 7Human-robot Cooperation Based on Visual Communication4 citations · 2020
- 8A Robust Face Tracking Method by Employing Color-based Particle Filter4 citations · 2011
- 9Fruits and Vegetables Detection using the Improved YOLOv33 citations · 2022
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