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Total Citations
2
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About
Alban Goupil is a robotics researcher whose work focuses on computer vision and autonomous perception systems, particularly for humanoid robots in competitive environments like the RoboCup. His most-cited paper, "Accurate Football Detection and Localization for Nao Robot with the Improved HOG-SVM Approach" (2018), addresses the challenge of real-time object detection under severe computational constraints. By enhancing the classic HOG-SVM machine learning method, Goupil demonstrated how robust visual processing could be achieved on limited hardware, significantly improving a robot’s ability to locate and track a football during dynamic play. This contribution is critical for advancing autonomous decision-making in robotics, bridging the gap between traditional rule-based systems and modern learning-based approaches. While his citation count remains modest—reflecting the niche, applied nature of his work—Goupil’s research holds practical value for the RoboCup community and embedded vision systems. His efforts highlight the importance of efficient, real-world solutions in robotics, making him a notable figure in the intersection of machine learning and autonomous perception.
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