S. Maheswari
Papers
1
Total Citations
3
H-Index
1
About
Dr. S. Maheswari is a dedicated researcher in computer vision and pattern recognition, with a primary focus on human action recognition for intelligent surveillance systems. Her work addresses critical challenges in video analysis, including automated classification of human movements for applications ranging from security monitoring to human-robot interaction. Her most cited paper, "RVM-based human action classification through Gabor and Haar feature extraction" (2015), introduces a novel methodology that combines Gabor and Haar feature extraction techniques with Relevance Vector Machine (RVM) classification to improve the accuracy and efficiency of action recognition in video streams. This work, which has garnered 3 citations, demonstrates her ability to integrate classical feature descriptors with advanced machine learning algorithms. Dr. Maheswari’s research is particularly motivated by real-world applications such as video retrieval, surveillance, and assistive technologies for deaf and dumb individuals, highlighting her commitment to socially impactful computer vision. Her contributions provide a foundation for more robust and computationally efficient action recognition systems, making her a valuable voice in the growing field of automated video understanding.
Research Focus
Key Achievements
Top Papers
- 1