Xukuai Liu

Papers

Total Citations

H-Index

About

Xukuai Liu is a researcher whose work centers on computer vision and autonomous systems, with a particular focus on object tracking algorithms for unmanned aerial vehicles (UAVs). Liu’s most-cited paper, “Object Tracking Algorithm of UAV Based on Fast Kernel Correlation Filter” (2020), introduces a computationally efficient tracking method that leverages kernelized correlation filters to enhance real-time performance in dynamic aerial environments. This contribution addresses a critical challenge in UAV navigation and surveillance: maintaining robust object tracking despite rapid motion, occlusions, and changing backgrounds. Although the paper has garnered 3 citations to date, it represents a foundational step in optimizing lightweight tracking algorithms for resource-constrained platforms. Liu’s work is notable for its practical emphasis on speed and accuracy trade-offs, making it relevant to researchers developing autonomous drones, robotics, and edge-computing vision systems. By advancing kernel correlation filter techniques, Liu contributes to the broader goal of enabling reliable, real-time visual intelligence in mobile and aerial applications—a key area of growth in modern computer vision and robotics research.