Yuyao Min

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About

Yuyao Min is a rising researcher in computer vision and intelligent surveillance systems, with a primary focus on advancing human-centric detection and tracking technologies. Their most notable contribution is the development of an improved YOLOv7 algorithm for human target detection and tracking, published in 2023. This work addresses critical limitations in mainstream detection frameworks by enhancing detection accuracy, a persistent challenge in real-world applications ranging from medical monitoring to security surveillance. The algorithm refines YOLOv7’s architecture to better handle complex scenarios, such as occlusions and varying lighting conditions, thereby improving the reliability of human tracking in dynamic environments. Although the paper has garnered 3 citations since its recent publication, its practical relevance is underscored by the growing demand for robust, real-time tracking systems in fields like autonomous navigation and public safety. Min’s research bridges the gap between state-of-the-art detection models and their deployment in mission-critical contexts, offering a scalable solution for human-oriented tracking. As an emerging scholar, Min’s work signals a promising trajectory in applied AI, with potential to influence next-generation surveillance and assistive technologies.