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）
Research in Anti-UAV (Unmanned Aerial Vehicle) tracking has explored various modalities, including RGB, TIR, and RGB-T fusion. However, a unified framework for cross-modal collaboration is still lacking. Existing approaches have primarily focused on independent models for individual tasks, often overlooking the potential for cross-modal information sharing. Furthermore, Anti-UAV tracking techniques are still in their infancy, with current solutions struggling to achieve effective multimodal data fusion. To address these challenges, we propose UAUTrack, a unified single-target tracking framework built upon a single-stream, single-stage, end-to-end architecture that effectively integrates multiple modalities. UAUTrack introduces a key component: a text prior prompt strategy that directs the model to focus on UAVs across various scenarios. Experimental results show that UAUTrack achieves state-of-the-art performance on the Anti-UAV and DUT Anti-UAV datasets, and maintains a favourable trade-off between accuracy and speed on the Anti-UAV410 dataset, demonstrating both high accuracy and practical efficiency across diverse Anti-UAV scenarios.
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…
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）
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…
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…
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…
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）
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 joint…
Although short-term fully occlusion happens rare in visual object tracking, most trackers will fail under these circumstances. However, humans can still catch up the target by anticipating the trajectory of the target even the target is invisible. Recent psychology also has shown that humans build the mental image of the future. Inspired by that, we present a HAllucinating Features to Track (HAFT) model that enables to forecast the visual feature embedding of future frames. The anticipated future frames focus on the movement of the target while hallucinating the occluded part of the target. Jo…
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）
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）
