Papers
2
Total Citations
23
H-Index
2
About
Yawen Cui’s research lies at the intersection of computer vision, deep learning, and multi-robot systems, with a central focus on visual target tracking. Her work addresses a fundamental challenge in robotics: enabling autonomous agents to track a moving target using only monocular image sensing, without relying on expensive onboard localization equipment. In her highly cited 2018 paper, she introduced the “learn-to-track” (LtT) system, an end-to-end deep CNN-based framework that constructs a fully autonomous visual tracking pipeline from a single camera view. This work, with 12 citations, marked a shift from traditional control-based approaches to learning-driven perception. Her 2017 study extended this concept to multi-robot systems, demonstrating how deep convolutional neural networks can enable one-on-one target tracking across distributed robotic platforms—a contribution that has garnered 11 citations. By pioneering deep learning solutions for monocular visual tracking in robotics, Cui has helped democratize autonomous tracking capabilities, making them accessible even for robots with limited onboard sensors. Her research continues to influence the development of lightweight, intelligent vision systems for autonomous navigation and collaborative robotics.
Research Focus
Key Achievements
Top Papers
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