SangKyu Kang
Papers
2
Total Citations
91
H-Index
2
About
SangKyu Kang is a researcher whose work bridges computer vision, autonomous robotics, and infrastructure monitoring. His key research areas include real-time object tracking, optical flow algorithms, and automated pavement distress detection. Kang’s major contribution lies in developing a non-prior training active feature model for optical flow-based object tracking, a method that enables robust real-time performance without requiring extensive pre-training datasets. This work, published in 2005, has accumulated 88 citations, demonstrating its lasting influence in the field of visual tracking. Additionally, Kang pioneered the use of GPS Virtual Reference Station (VRS) technology to guide an autonomous robot for pavement distress surveys, addressing critical infrastructure maintenance needs. This system can automatically detect and classify cracks, potholes, and other surface defects, offering a safer and more efficient alternative to traditional manual inspections. While this 2008 paper has fewer citations, it represents an innovative application of robotics to civil engineering challenges. Kang’s work exemplifies how computer vision and autonomous systems can solve real-world problems, making him a notable figure in applied robotics and intelligent infrastructure monitoring.
Research Focus
Key Achievements
Top Papers
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