Nabil Zerrouki

University of Sciences and Technology Houari Boumediene

Papers

Total Citations

H-Index

About

Nabil Zerrouki is a leading researcher in computer vision and human activity recognition, with a particular focus on intelligent healthcare and assistive technologies. His work centers on developing robust algorithms for the automatic analysis of human motion, aiming to enhance autonomous systems for medical diagnosis and elderly care. His most cited paper, "Vision-Based Human Action Classification Using Adaptive Boosting Algorithm" (2018, 92 citations), introduced an innovative method for recognizing human actions by analyzing variations in body shape. This work demonstrated how adaptive boosting could effectively classify complex movements, laying a foundation for more responsive and reliable surveillance and monitoring systems. Zerrouki’s contributions are pivotal in bridging the gap between raw visual data and meaningful behavioral interpretation, enabling safer, smarter environments for vulnerable populations. His research continues to influence the design of real-time, vision-driven applications that prioritize both accuracy and computational efficiency.