Geng Lu
Tsinghua University
48 Papers
227 Citations
Geng Lu is an academic researcher from Tsinghua University. The author has contributed to research in topics: Robust control & Control theory. The author has an hindex of 19, co-authored 48 publications.
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Papers
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization
Yiming Li,Changhong Fu,Fangqiang Ding,Ziyuan Huang,Geng Lu +4 more
- 14 Jun 2020
TL;DR: AutoTrack as mentioned in this paper proposes to online automatically and adaptively learn spatio-temporal regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters.
Formation Control for High-Order Linear Time-Invariant Multiagent Systems With Time Delays
TL;DR: An approach to expand the feasible formation set and an algorithm to design the protocol for multiagent systems to achieve time-varying formations are provided, respectively and numerical simulations are presented to demonstrate theoretical results.
201
Formation-containment analysis and design for high-order linear time-invariant swarm systems
TL;DR: In this article, the authors presented protocols for leaders and followers respectively to drive the states of leaders to realize the predefined time-varying formation and propel the followers to converge to the convex hull formed by the leaders.
131
Evaluation of Effect of Number of Electrodes in ECT Sensors on Image Quality
TL;DR: In this article, the effect of the number of electrodes on the quality of reconstructed images was investigated using the projected Landweber iteration algorithm based on both linear and semi-linear ECT models.
110
Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization
TL;DR: A novel tracking approach based on a multi-regularized correlation filter (MRCF) enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability in unmanned aerial vehicle (UAV) tracking.
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