Daoxun Xia
Papers
1
Total Citations
11
H-Index
1
About
Daoxun Xia is a researcher whose work sits at the intersection of computer vision and human behavior analysis, with a particular focus on pose estimation and object detection. His most cited paper, "Learning rich features from objectness estimation for human lying-pose detection" (2016, 11 citations), introduces a novel approach that leverages objectness estimation—a technique typically used for generic object detection—to extract rich, discriminative features specifically tailored for detecting human lying poses. This contribution is notable for bridging the gap between general object detection and specialized human pose recognition, offering a method that improves accuracy in challenging scenarios such as occluded or cluttered environments. While his citation count reflects a focused, early-stage impact, Xia’s work demonstrates a thoughtful integration of established computer vision principles with niche application needs, making it a valuable reference for researchers exploring pose detection in healthcare, surveillance, or sports analytics. His approach underscores the potential of adapting generic detection frameworks to solve domain-specific problems, a strategy that continues to inspire efficient feature learning in human-centric vision tasks.
Research Focus
Key Achievements
Top Papers
- 1