Geng Lu

Papers

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About

Geng Lu is a leading researcher in visual tracking and autonomous systems, with a particular focus on unmanned aerial vehicle (UAV) navigation and perception. His most cited work, "Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization" (2021, 84 citations), addresses critical challenges in model-free tracking by enhancing discriminative correlation filter (DCF)-based methods. Lu’s key contribution lies in developing multi-regularization techniques that overcome the limitations of traditional DCFs, which rely solely on cyclic shift operations from current frames for filter training. By integrating spatial and temporal constraints, his approach significantly improves tracking robustness and localization accuracy in dynamic UAV environments—a breakthrough for real-time aerial applications. This work has been widely adopted in robotics and computer vision communities, with citations spanning autonomous navigation, surveillance, and drone self-localization systems. Lu’s research bridges theoretical advances in correlation filtering with practical deployment constraints, making him a pivotal figure in advancing UAV autonomy. His ongoing work continues to shape how drones perceive and interact with complex environments, with implications for search-and-rescue, agriculture, and smart city infrastructure.