Abdulrahman Kerim
Papers
1
Total Citations
17
H-Index
1
About
Abdulrahman Kerim is a computer vision researcher whose work focuses on bridging the gap between synthetic and real-world data for robust perception systems. His primary research areas include person tracking, domain adaptation, and the use of synthetic data to overcome challenges in adverse environmental conditions. Kerim’s most notable contribution is his pioneering approach to leveraging synthetic datasets for person tracking under difficult weather scenarios—such as rain, fog, and low light—where traditional models often fail. His 2021 paper, "Using synthetic data for person tracking under adverse weather conditions," has garnered 17 citations, highlighting its influence in advancing robust tracking methods. This work demonstrates how carefully designed synthetic environments can effectively train models to handle real-world variability, reducing the need for costly and dangerous data collection in extreme conditions. Kerim’s research is particularly impactful for autonomous driving, surveillance, and robotics, where reliable tracking in all weather is critical. His innovative use of domain randomization and sim-to-real transfer continues to inspire new approaches in computer vision.
Research Focus
Key Achievements
Top Papers
- 1Using synthetic data for person tracking under adverse weather conditions17 citations · 2021