Papers
15
Total Citations
371
H-Index
9
About
Dr. Fouzi Harrou is a leading researcher at the intersection of computer vision, swarm robotics, and intelligent fault detection. His work is defined by a dual focus: enabling autonomous systems to perceive their environment and ensuring their reliable operation. In computer vision, Dr. Harrou has made significant contributions to human action recognition, developing an adaptive boosting algorithm for precise classification based on body shape variations (92 citations), and pioneering deep-learning-based stereovision for unsupervised obstacle detection in driving environments (85 citations). His impact is equally profound in swarm robotics, where he has introduced novel topological approaches—such as the Distance-Minkowski k-Nearest Neighbors (DM-KNN) method—to improve aggregation and pattern formation in robot swarms. Dr. Harrou has also advanced the critical field of data-driven fault detection, creating robust strategies to monitor swarm systems under noisy conditions and employing ensemble learning for motion speed prediction. His recent work explores the application of swarm robotics for sustainable environmental monitoring. With multiple papers on self-organization and fault tolerance, Dr. Harrou’s research is foundational for building safer, more efficient, and truly autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Vision-Based Human Action Classification Using Adaptive Boosting Algorithm92 citations · 2018
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- 3Self-organization in aggregating robot swarms: A DW-KNN topological approach42 citations · 2018
- 4Monitoring a robot swarm using a data-driven fault detection approach42 citations · 2017
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- 8Toward Emerging Cubic-Spline Patterns With a Mobile Robotics Swarm System11 citations · 2021
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