Yawar Abbas
Papers
2
Total Citations
54
H-Index
2
About
Yawar Abbas is a leading researcher in computer vision and artificial intelligence, with a primary focus on human activity recognition (HAR) in drone-captured video. His work addresses the critical challenge of enabling machines to understand and interpret human behavior from aerial perspectives, with applications in video surveillance, human-robot collaboration, sports analysis, and public safety. Abbas’s most cited paper, "Unmanned Aerial Vehicles for Human Detection and Recognition Using Neural-Network Model" (2024, 31 citations), introduces a neural-network framework for detecting and recognizing human actions in UAV footage, tackling the immense diversity in human movement and appearance. His subsequent work, "Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier" (2025, 23 citations), advances the field by integrating 3D point cloud data with a neuro-fuzzy classifier to improve action recognition accuracy in complex, real-world environments. Collectively, his research has garnered over 54 citations, demonstrating significant impact in the rapidly growing domain of aerial surveillance and autonomous systems. Abbas’s contributions are notable for bridging deep learning with traditional fuzzy logic, offering robust solutions for dynamic, unconstrained scenarios. His work is essential reading for students and researchers interested in UAV-based HAR, 3D vision, and intelligent surveillance systems.
Research Focus
Key Achievements
Top Papers
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