Asif Rahim

Papers

Total Citations

H-Index

About

Dr. Asif Rahim is a computer vision researcher whose work centers on human action recognition (HAR) in video sequences, with a particular focus on enhancing surveillance and security systems. His most cited paper, "Human Action Recognition in Video Sequence using Logistic Regression by Features Fusion Approach based on CNN Features" (2021), has garnered 17 citations and addresses a critical challenge in real-world applications: accurately identifying human activities from video data. Rahim's key contribution lies in developing a features fusion approach that combines CNN-derived features with logistic regression, significantly improving the robustness of action classification in dynamic environments like video surveillance. This work has direct implications for automated monitoring systems, enabling them to detect suspicious movements or generate alerts for undesirable situations with greater precision. By bridging deep learning and traditional machine learning methods, Rahim has advanced the practical deployment of HAR in robotics and security contexts. His research demonstrates a clear commitment to solving real-world problems, making his work valuable for both academic researchers and industry practitioners seeking to build more intelligent, responsive video analysis systems.