Evan Quinn

Papers

Total Citations

H-Index

About

Evan Quinn is a researcher at the intersection of computer vision and sports analytics, with a focused expertise in Human Action Recognition (HAR) for combat sports. His primary contribution lies in developing automated systems that can analyze video footage of competitions and training sessions in real time, accurately classifying complex human movements. Quinn’s most cited work, "Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis" (2022, 7 citations), introduces a prototype automation client that leverages advanced CV architectures to interpret dynamic athletic actions. This research bridges the gap between theoretical computer vision models and practical, high-stakes applications, offering coaches and athletes a tool for performance analysis and strategy refinement. By tackling the challenges of real-time processing and movement classification in fast-paced environments, Quinn has laid groundwork for more responsive and intelligent sports technology. His work not only advances the field of HAR but also demonstrates the broader potential of automated video analysis in specialized domains. With growing interest in AI-driven sports analytics, Quinn’s contributions are poised to influence both academic research and practical training methodologies.