Ahmed Atwan
Papers
1
Total Citations
1
H-Index
1
About
Ahmed Atwan is a researcher whose work sits at the intersection of computer vision and intelligent surveillance, with a particular focus on pedestrian detection and tracking. His key research areas include deep learning-based object detection, occlusion handling, and the application of convolutional neural networks to real-world security and robotics challenges. Atwan’s most notable contribution is his 2019 paper, "Multiple Pedestrian Detection Depending on Faster Region-based Convolutional Neural Network (RCNN)," which proposed a novel framework to address the persistent challenge of occlusion in multi-person tracking. By leveraging the high accuracy of Faster R-CNN, his work aimed to improve detection reliability in complex environments such as crowded streets or surveillance feeds. While the paper has garnered 1 citation to date, it represents a foundational step in applying state-of-the-art deep learning architectures to practical, safety-critical systems. Atwan’s research is particularly relevant for students and engineers working on autonomous vehicles, robotics, and security systems, where robust pedestrian detection is essential. His work underscores the ongoing need for efficient, occlusion-resistant models in dynamic visual environments.
Research Focus
Key Achievements
Top Papers
- 1