Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking. Specifically, ADTrack adopts dual regression, where the context filter and the target-focused filter restrict each other for dual filter learning. Exhaustive experiments are conducted on typical dark sceneries benchmark, consisting of 37 typical night sequences from authoritative benchmarks, i.e., UAVDark, and our newly constructed benchmark UAVDark70. The results have shown that ADTrack favorably outperforms other state-of-the-art trackers and achieves a real-time speed of 34 frames/s on a single CPU, greatly extending robust UAV tracking to night scenes.
Visual object tracking, which is representing a major interest in image processing field, has facilitated numerous real world applications. Among them, equipping unmanned aerial vehicle (UAV) with real time robust visual trackers for all day aerial maneuver, is currently attracting incremental attention and has remarkably broadened the scope of applications of object tracking. However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers' capabilities to be deployed in the dark. In darkness, the conditions can be more complex and…
University of Chinese Academic of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of North Electronic Equipment（北方电子设备研究所）；Baidu Research（百度研究院）
Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work, we consider the task of tracking UAVs, providing rich information such as location and trajectory. To facilitate research on this topic, we propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes. The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs. Furthermore, the advancement of addressing research challen…
How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；CloudWalk Technology Co., Ltd（云walk科技有限公司）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；School of Aeronautics and Astronautics, Sun Yat-sen University（中山大学航空宇航学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System
Jiangnan University（江南大学）；Institute of North Electronic Equipment（北方电子设备研究所）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）；Peng Cheng Laboratory（鹏城实验室）；National University of Singapore（新加坡国立大学）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；Beijing Institute of Technology（北京理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）；PhiGent Robotics（鉴智机器人）；Tsinghua University（清华大学）
Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough to detect and track invading UAVs. Thus we propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV for short. It contains a detection dataset with a total of 10,000 images and a tracking d…
Beijing University of Posts and Telecommunications（北京邮电大学）；Peng Cheng Laboratory（鹏城实验室）
Unmanned aerial vehicles (UAVs) are widely applied in civil applications, such as disaster relief, agriculture and cargo transportation, etc. With the massive number of UAV flight activities, the anti-collision technologies aiming to avoid the collisions between UAVs and other objects have attracted much attention. The anti-collision technologies are of vital importance to guarantee the survivability and safety of UAVs. In this article, a comprehensive survey on UAV anti-collision technologies is presented. We firstly introduce laws and regulations on UAV safety which prevent collision at the …
The emerging global market for unmanned aerial vehicle (UAV) services is anticipated to reach USD 58.4 billion by 2026, spurring significant efforts to safely integrate routine UAV operations into the national airspace in a manner that they do not compromise the existing safety levels. The commercial use of UAVs would be enhanced by an ability to sense and avoid potential mid-air collision threats however research in this field is hindered by the lack of available datasets as they are expensive and technically complex to capture. In this paper we present a dataset for vision based aircraft det…
The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localization at the cost of long and varying computation time in various situations, which often necessitates the use of powerful ground station machines. In order to make image-based geo-localization online and pragmatic for lightweight embedded systems on UAVs, we propose a framework tha…
Fraunhofer IOSB（弗劳恩霍夫应用光学、系统技术和图像评估研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；University of Nevada, Las Vegas（内华达大学拉斯维加斯分校）
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications for motion prediction. On the one hand, these models can capture complex object dynamics with less modeling required, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, we present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. Since U…
Future-Aware Flow Planning for Safe UAV Target Following
Stockholm University（斯德哥尔摩大学）；Peking University（北京大学）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）；Guangdong Provincial/Zhuhai Key Laboratory of IRADS（广东省/珠海市智能机器人先进感知与决策系统重点实验室）
This paper systematically studies the cooperative area coverage and target tracking problem of multiple-unmanned aerial vehicles (multi-UAVs). The problem is solved by decomposing into three sub-problems: information fusion, task assignment, and multi-UAV behavior decision-making. Specifically, in the information fusion process, we use the maximum consistency protocol to update the joint estimation states of multi-targets (JESMT) and the area detection information. The area detection information is represented by the equivalent visiting time map (EVTM), which is built based on the detection pr…
Infrared UAV Target Tracking with Dynamic Feature Refinement and Global Contextual Attention Knowledge Distillation
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Xi’an Modern Control Technology Research Institute（西安现代控制技术研究所）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）
With the characteristics of vertical take-off and landing and long endurance, tiltrotor has attracted considerable attention in recent decades for its potential applications in civil and scientific research. However, the problems of strong couplings, nonlinear characteristics and mismatched disturbances inevitably exist in the tiltrotor, which bring great challenges to the controller design in transition mode. In this paper, we combined a super-twisting extended state observer (STESO) with an adaptive recursive sliding mode control (ARSMC) together to design a tiltrotor aircraft attitude syste…
Veermata Jijabai Technological Institute (VJTI)（维尔玛塔·吉贾拜技术学院）
The necessity of maintaining a robust antiterrorist task force has become imperative in recent times with resurgence of rogue element in the society. A well equipped combat force warrants the safety and security of citizens and the integrity of the sovereign state. In this paper we propose a novel teleoperating robot which can play a major role in combat, rescue and reconnaissance missions by substantially reducing loss of human soldiers in such hostile environments. The proposed robotic solution consists of an unmanned ground vehicle equipped with an IP camera visual system broadcasting real-…
Passive Fault Tolerance through Tension-to-Thrust Feed-Forward: Hybrid Input-to-State Stability for Decentralized Multi-UAV Slung-Load Transport under Abrupt Cable Severance
xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
School of Electronic and Electrical Engineering, University of Leeds（利兹大学电子与电气工程学院）；College of Electronics Engineering, Ninevah University（尼尼微大学电子工程学院）
Aerial Target Encirclement and Interception with Noisy Range Observations
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore（电子工程学院，南洋理工大学，新加坡）；School of Automation, Guangdong University of Technology, Guangzhou 510006, China（自动化学院，广东技术大学，广州）
This article presents a unique framework for deploying decentralized and infrastructure-independent swarms of homogeneous aerial vehicles in the real world without explicit communication. This is a requirement in swarm research, which anticipates that global knowledge and communication will not scale well with the number of robots. The system architecture proposed in this article employs the UVDAR technique to directly perceive the local neighborhood for direct mutual localization of swarm members. The technique allows for decentralization and high scalability of swarm systems, such as can be …
Institute of North Electronic Equipment（北电子设备研究所）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunication（北京邮电大学）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Jiaotong University（北京交通大学）；Qihoo 360（奇虎360）；OPPO Research Institute（OPPO研究院）；Institute of Information Engineering, CAS（中国科学院信息工程研究所）；Institute of Deep Learning, Baidu Research（百度深度学习研究所）
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two subsets in the dataset, $i.e.$, the test-dev subset and test-challenge subset. Both subsets consist of 140 thermal infrared video sequences, spanning multiple occurrences of multi-scale UAVs. Around 24 participating teams from the globe competed in the 2nd Anti-UAV Challenge. In this paper, we provide a brief summary of the 2nd Anti-UAV Workshop \& Challenge incl…
UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking
College of Intelligent Robotics and Advanced Manufacturing（智能机器人与先进制造学院）；Fudan University（复旦大学）；School of Computer Science and Technology（计算机科学与技术学院）；Zhejiang Normal University（浙江师范大学）；Department of Mechanical Engineering（机械工程系）；The Hong Kong Polytechnic University（香港理工大学）
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
National Innovation Institute of Defense Technology（国防科技大学全国创新研究院）；Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunication（北京邮电大学）；National University of Singapore（新加坡国立大学）；Alibaba Inc.（阿里巴巴集团）；University of Amsterdam（阿姆斯特丹大学）；Jiangnan University（江南大学）；PhiGent Robotics（PhiGent机器人公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Nanjing University of Science and Technology（南京理工大学）；Wuhan University（武汉大学）；Alibaba DAMO Academy（阿里巴巴达摩院）；University of Tokyo（东京大学）；Tsinghua University（清华大学）；Pensees Singapore（新加坡Pensees公司）；Peng Cheng Laboratory（鹏城实验室）；Bosch Delta Lab（博世德尔塔实验室）；VIS Lab, University of Amsterdam（阿姆斯特丹大学VIS实验室）；Intelligent Game and Decision Laboratory（智能游戏与决策实验室）
Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges
College of Computer Science and Engineering, Guilin University of Technology, China（桂林理工大学计算机科学与工程学院）；Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, China（广西嵌入式技术与智能系统重点实验室）；JD Logistic, Beijing, China（京东物流）
FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（教育部光电成像技术与系统重点实验室）；Chongqing Innovation Center, Beijing Institute of Technology（北京理工大学重庆创新中心）
Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；ShanghaiTech University（上海科技大学）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）
