Hanling Zhang
Papers
1
Total Citations
16
H-Index
1
About
Hanling Zhang is a researcher in computer vision and human action recognition, with a focus on depth-skeleton feature analysis and sparse representation methods. Her most cited work, "Combining depth-skeleton feature with sparse coding for action recognition" (2016, 16 citations), introduces a novel framework that integrates depth-skeleton data with sparse coding techniques to improve the accuracy and robustness of action recognition systems. This contribution addresses key challenges in understanding human movements from video data, offering a computationally efficient approach that reduces noise and enhances feature discriminability. Zhang's research has implications for applications in surveillance, human-computer interaction, and assistive technologies. Her work demonstrates a strong commitment to advancing machine learning methodologies for real-world visual understanding tasks.
Research Focus
Key Achievements
Top Papers
- 1Combining depth-skeleton feature with sparse coding for action recognition16 citations · 2016