Felipe dos Anjos Rezende
Papers
1
Total Citations
2
H-Index
1
About
Felipe dos Anjos Rezende is a researcher at the intersection of computer vision, sports analytics, and unmanned aerial vehicle (UAV) technology. His work focuses on developing automated methods for tracking and analyzing athlete performance using aerial imagery, with a particular emphasis on soccer. In his most cited study, "Soccer Player Tracking Using UAV Imagery: A Comparative Study of Yolo and Traditional Image Processing Algorithms" (2025, 2 citations), Rezende systematically evaluates the effectiveness of deep learning-based object detection (YOLO) against classical image processing techniques for real-time player tracking. This work demonstrates that modern neural network approaches significantly outperform traditional algorithms in accuracy and robustness, even under challenging conditions like occlusions and rapid movements. By bridging UAV technology with sports science, Rezende’s research provides coaches and analysts with a scalable, cost-effective tool for tactical analysis and performance evaluation. His contributions are particularly valuable for advancing automated sports analytics, enabling more precise insights into team dynamics and player positioning. As a rising voice in applied computer vision, Rezende’s work holds promise for transforming how athletic performance is studied and optimized.
Research Focus
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Top Papers
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