Yuanjie Dang

Papers

Total Citations

107

H-Index

About

Yuanjie Dang is a leading researcher in autonomous systems, computer vision, and real-time object tracking, with a particular focus on resource-constrained platforms like drones. His most-cited work, "Real-Time Object Tracking on a Drone With Multi-Inertial Sensing Data" (2017, 107 citations), addresses a long-standing challenge in dynamic environments by introducing a lightweight, onboard tracking approach. This method leverages multi-inertial sensing data to achieve robust, real-time performance without relying on powerful off-board computation, marking a significant advance in drone autonomy. Dang’s contributions have been instrumental in bridging the gap between theoretical tracking algorithms and practical deployment on small, energy-limited aerial vehicles. His work is widely cited in the fields of robotics, UAV navigation, and embedded vision systems, reflecting its impact on both academic research and industrial applications. By enabling drones to perform complex tracking tasks in real time, Dang has helped pave the way for more intelligent and responsive autonomous systems, making his research essential reading for students and engineers working at the intersection of sensing, control, and onboard intelligence.