Xiuli Chen

Papers

Total Citations

H-Index

About

Xiuli Chen is a computer vision researcher whose work centers on human action recognition, a field critical to advancing robotics, video surveillance, and human-computer interaction. Her most-cited paper, "Action recognition using Correlogram of Body Poses and spectral regression" (2011, 26 citations), introduces a novel representation for human actions by leveraging the Correlogram of Body Poses. This approach captures spatial-temporal relationships between body poses, enabling more robust and efficient action classification through spectral regression techniques. By addressing the challenge of recognizing complex human movements in dynamic environments, Chen’s contribution provides a foundation for applications ranging from intelligent user interfaces to multimedia retrieval. Her work demonstrates a keen ability to merge pose-based features with machine learning, offering a computationally effective solution to a persistent problem in computer vision. With 26 citations, this paper has influenced subsequent research in action recognition, reflecting its value in both theoretical and applied contexts. Chen’s research continues to inspire students and researchers exploring the intersection of pose estimation, pattern recognition, and real-world vision systems.