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Article
Robotics
Anibal Ollero et al.
Summary: This article primarily analyzes the evolution and current trends in aerial robotic manipulation, covering different types of aircraft equipped with various robotic manipulators, including cooperative manipulation and multibody design, and finally presenting the vision of future new generation aerial robotic manipulators.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Automation & Control Systems
Jiacheng Liang et al.
Summary: This article presents a trajectory tracking control method for the unmanned aerial manipulator robot system (UAMRS) under model uncertainty and unknown disturbances. A low-complexity prescribed performance controller is proposed to effectively reduce design complexity and achieve better performance, with the use of a nonlinear disturbance observer to estimate and compensate uncertainties and external disturbances.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Proceedings Paper
Automation & Control Systems
Guangze Zheng et al.
Summary: This paper proposes a novel Siamese network for vision-based UAM approaching, addressing the issue of object tracking in the presence of scale variation. It introduces pairwise scale-channel attention and scale-aware anchor proposal to effectively deal with the challenges. A new UAM tracking benchmark, UAMT100, is also provided for evaluation.
2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Junjie Ye et al.
Summary: This work presents an unsupervised domain adaptation framework for nighttime aerial tracking, which includes a unique object discovery approach and feature alignment layers. It also introduces a benchmark dataset for nighttime tracking. The experiments demonstrate the robustness and adaptability of the framework in nighttime aerial tracking.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Article
Computer Science, Artificial Intelligence
Ning Wang et al.
Summary: The advancement of visual tracking through deep learning models has been achieved with the proposal of an unsupervised learning method, focusing on bidirectional tracking and training with unlabeled videos to achieve baseline accuracy of fully supervised trackers with real-time speed. Additionally, the unsupervised framework shows potential in leveraging more unlabeled or weakly labeled data for further improvement in tracking accuracy.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Article
Computer Science, Artificial Intelligence
Lianghua Huang et al.
Summary: GOT-10k is a large tracking database that covers over 560 classes of moving objects and 87 motion patterns, providing a unified training and evaluation platform for tracker development. Additionally, the introduction of a one-shot protocol for tracker evaluation avoids biased results towards familiar objects and promotes generalization in tracker development.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Bin Yan et al.
Summary: Object tracking has made significant progress in recent years, but state-of-the-art trackers are becoming heavier and more expensive. LightTrack, utilizing neural architecture search, has been designed to be more lightweight and efficient, with experimental results showing its effectiveness in resource-constrained environments.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Computer Science, Artificial Intelligence
Ramprasaath R. Selvaraju et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Lichao Zhang et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
(2019)
Article
Automation & Control Systems
Hyeonbeom Lee et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Esteban Real et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Qing Guo et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Article
Computer Science, Artificial Intelligence
Olga Russakovsky et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2015)
