Thayron M. Hudson
Papers
1
Total Citations
2
H-Index
1
About
Thayron M. Hudson is a rising researcher at the intersection of computer vision and sports analytics, with a primary focus on leveraging unmanned aerial vehicle (UAV) imagery for real-time player tracking. His most-cited work, "Soccer Player Tracking Using UAV Imagery: A Comparative Study of Yolo and Traditional Image Processing Algorithms" (2025), systematically evaluates the performance of deep learning-based YOLO detectors against classical image processing techniques for tracking soccer players from aerial footage. This study provides critical insights into the trade-offs between accuracy and computational efficiency, demonstrating that while YOLO offers superior detection robustness, traditional methods remain viable for resource-constrained applications. Though early in his career, Hudson’s work addresses a pressing need in tactical analysis and performance evaluation, enabling coaches and analysts to extract detailed movement patterns and team dynamics without expensive ground-based camera systems. With 2 citations already, his research is gaining traction among sports technology and computer vision communities. Hudson’s contributions are particularly notable for bridging the gap between cutting-edge AI and practical sports science, offering a scalable solution for amateur and professional teams alike. His ongoing work promises to further advance autonomous sports analytics.
Research Focus
Key Achievements
Top Papers
- 1