Yuerong Zhao

Papers

Total Citations

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About

Yuerong Zhao is a researcher in computer vision and pattern recognition, with a primary focus on video-based human action recognition and multifeature fusion techniques. Their most-cited work, "Multifeature fusion action recognition based on key frames" (2021, 9 citations), addresses the growing commercial and academic demand for robust action recognition by proposing a method that integrates multiple visual features extracted from key frames. This approach improves recognition accuracy while reducing computational redundancy, offering a practical solution for real-world applications such as surveillance, human-computer interaction, and sports analytics. Zhao’s contributions lie in advancing the efficiency and reliability of action recognition systems, particularly through the strategic selection of key frames and the fusion of complementary features. While their citation count is modest, their work reflects a solid foundation in a rapidly evolving field, demonstrating potential for further impact as video-based technologies continue to expand. Zhao’s research is valuable for students and practitioners seeking to understand the intersection of feature engineering and temporal modeling in computer vision.