Chiung-Yao Fang
Papers
3
Total Citations
33
H-Index
3
About
Chiung-Yao Fang is a computer vision and robotics researcher whose work centers on developing intelligent perception systems for autonomous and companion robots. Fang's primary contributions lie in vision-based human action recognition, with a particular focus on enabling robots to understand and respond to human behavior in real-world, dynamic environments. Their research tackles one of the field's most persistent challenges: accurately recognizing human actions even when the observing camera is itself in motion — a critical requirement for mobile robotic platforms. Fang's methodology has evolved progressively, moving from structured depth-and-color image pipelines using Kinect sensors to sophisticated deep learning architectures capable of online, real-time recognition. Their 2018 system, which earned 13 citations, established a foundational three-stage framework integrating motion map construction, feature extraction, and action classification. Subsequent work in 2019 and 2021, accumulating 11 and 9 citations respectively, extended this foundation to handle moving cameras and indoor smart robot applications. Together, these contributions form a coherent research trajectory advancing human-robot interaction. Fang's work holds significant practical value for companion robotics, assistive technology, and smart indoor environments, making it a meaningful reference point for researchers exploring embodied AI and real-time human sensing.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3