Sumaira Ghazal
Papers
1
Total Citations
41
H-Index
1
About
Sumaira Ghazal is a leading researcher in computer vision and human activity recognition (HAR), with a focus on developing intelligent systems for video surveillance, robot navigation, and ambient intelligence. Her most cited work, "Human activity recognition using 2D skeleton data and supervised machine learning" (2019, 41 citations), pioneers an innovative approach that leverages two-dimensional skeleton data—extracted from standard RGB cameras—rather than expensive depth sensors like Kinect. This breakthrough significantly lowers the barrier for real-world HAR deployment, making it more accessible for telecare and security applications. By combining supervised machine learning with efficient 2D pose estimation, Ghazal’s research demonstrates how to achieve robust activity classification without sacrificing accuracy. Her contributions have been instrumental in advancing cost-effective, vision-based HAR systems, earning her recognition as a key figure in the field. With over 40 citations on this seminal paper alone, Ghazal’s work continues to inspire new directions in automated human behavior analysis, bridging the gap between academic research and practical, scalable solutions for smart environments.
Research Focus
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Top Papers
- 1