Dian Yuan

Xidian University, Central South University

Papers

Total Citations

H-Index

About

Dian Yuan is a rising researcher in computer vision and robotics, whose work focuses on advancing visual tracking and navigation systems. Her key research areas include RGBT tracking, loop closure detection, and multi-modal perception for autonomous systems. Yuan’s most notable contribution is her work on temporal adaptive RGBT tracking with modality prompts, which addresses the critical challenge of fusing thermal and visible information for robust object tracking in complex environments. This work, published in 2024, has already garnered 61 citations, reflecting its timely impact on fields like robotics, surveillance, and autonomous driving. Earlier, Yuan made significant strides in mobile robot navigation with her 2019 paper on loop closure detection using multi-scale deep feature fusion, which has accumulated 31 citations. This approach improves pose estimation accuracy and reduces cumulative errors in SLAM systems. Yuan’s research bridges the gap between spatial and temporal information in tracking, offering practical solutions for real-world deployment. Her work is particularly valuable for students and researchers interested in multi-modal fusion, visual tracking, and autonomous navigation, demonstrating how deep learning can enhance perception in challenging environments.