Layer-Guided UAV Tracking: Enhancing Efficiency and Occlusion Robustness
Department of Control Science and Engineering, University of Shanghai for Science and Technology（上海理工大学控制科学与工程学院）；Busness School, University of Shanghai for Science and Technology（上海理工大学商学院）
Yang Zhou, Derui Ding, Ran Sun, Ying Sun, Haohua Zhang
Visual object tracking (VOT) plays a pivotal role in unmanned aerial vehicle (UAV) applications. Addressing the trade-off between accuracy and efficiency, especially under challenging conditions like unpredictable occlusion, remains a significant challenge. This paper introduces LGTrack, a unified UAV tracking framework that integrates dynamic layer selection, efficient feature enhancement, and robust representation learning for occlusions. By employing a novel lightweight Global-Grouped Coordinate Attention (GGCA) module, LGTrack captures long-range dependencies and global contexts, enhancing feature discriminability with minimal computational overhead. Additionally, a lightweight Similarity-Guided Layer Adaptation (SGLA) module replaces knowledge distillation, achieving an optimal balance between tracking precision and inference efficiency. Experiments on three datasets demonstrate LGTrack's state-of-the-art real-time speed (258.7 FPS on UAVDT) while maintaining competitive tracking accuracy (82.8\% precision). Code is available at https://github.com/XiaoMoc/LGTrack
Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
College of Computer Science and Engineering, Guilin University of Technology（桂林理工大学计算机科学与工程学院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）
V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University, China（福建省城市智能感知与计算重点实验室，厦门大学，中国）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China（多媒体可信感知与高效计算教育部重点实验室，厦门大学，中国）
Singapore University of Technology and Design（新加坡科技设计大学）；Shandong University（山东大学）
Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark - UAVHuman - for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequ…
SCT-MOT: Enhancing Air-to-Air Multiple UAVs Tracking with Swarm-Coupled Motion and Trajectory Guidance
China-UAE Belt and Road Joint Laboratory on Intelligent Unmanned Systems of School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学宇航学院中国-阿联酋‘一带一路’智能无人系统联合实验室）；School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学宇航学院）；School of Information and Computing Science, Xi'an Jiaotong-Liverpool University（西交利物浦大学信息与计算科学学院）
OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Tracking and Telecommunication Technology（北京跟踪与通信技术研究所）
3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment
Department of Computer Science and Engineering, Lehigh University（计算机科学与工程系，莱维大学）；Department of Civil and Environmental Engineering, Lehigh University（土木与环境工程系，莱维大学）
Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery
Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator i…
Enhancing UAV Search under Occlusion using Next Best View Planning
Swarm Exploration Group, Department of Communication Systems, Institute for Communications and Navigation, German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR)（德国航空航天中心（德意志航天中心））
University of Hertfordshire（赫特福德大学）；IDscan Biometrics（IDscan生物识别技术公司）
Despite the recent success of convolutional neural networks for computer vision applications, unconstrained face recognition remains a challenge. In this work, we make two contributions to the field. Firstly, we consider the problem of face recognition with partial occlusions and show how current approaches might suffer significant performance degradation when dealing with this kind of face images. We propose a simple method to find out which parts of the human face are more important to achieve a high recognition rate, and use that information during training to force a convolutional neural n…
Unmanned aerial vehicle (UAV) based visual tracking has been confronted with numerous challenges, e.g., object motion and occlusion. These challenges generally introduce unexpected mutations of target appearance and result in tracking failure. However, prevalent discriminative correlation filter (DCF) based trackers are insensitive to target mutations due to a predefined label, which concentrates on merely the centre of the training region. Meanwhile, appearance mutations caused by occlusion or similar objects usually lead to the inevitable learning of wrong information. To cope with appearanc…
Monocular Depth Guided Occlusion-Aware Disparity Refinement via Semi-supervised Learning in Laparoscopic Images
School of Life Science and Technology, Harbin Institute of Technology（生命科学与技术学院，哈尔滨工业大学）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（机器人与系统国家重点实验室，哈尔滨工业大学）
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）
Correlation filter (CF) has recently exhibited promising performance in visual object tracking for unmanned aerial vehicle (UAV). Such online learning method heavily depends on the quality of the training-set, yet complicated aerial scenarios like occlusion or out of view can reduce its reliability. In this work, a novel time slot-based distillation approach is proposed to efficiently and effectively optimize the training-set's quality on the fly. A cooperative energy minimization function is established to score the historical samples adaptively. To accelerate the scoring process, frames with…
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Dept. of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Institute of Software Chinese Academy of Science（中国科学院软件研究所）
Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to various special challenges in UAV tracking, \textit{e.g.}, severe occlusion and fast motion, most existing Siamese-based trackers hardly combine superior performance with high efficiency. To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking. By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN fo…
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）
Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects. Several approaches to enlarge search regions have been already proposed in the past years to make up for this shortcoming. However, with excessive background information, more background noises are also introduced and the discriminative filter is prone to learn from the ambiance rather than the object. This situation, along with appearance changes of objects caused by full/partial occlusion, illumination variation, and other reasons has made it more likely to have aberrances in the…
T-SiamTPN: Temporal Siamese Transformer Pyramid Networks for Robust and Efficient UAV Tracking
Department of Electrical Engineering, Amirkabir University of Technology (AUT)（电气工程系，阿米尔卡比尔技术大学）；Qatar Computing Research Institute Hamad Bin Khalifa University（卡塔尔计算研究所，哈马德·本·卡西姆大学）
University of Lincoln（林肯大学）；Guangzhou University（广州大学）
Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs). This is especially true for small or micro UAVs, due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion based visual neuron called the Lobula Giant Movement Detector (LGMD). The defining feature of the LGMD neuron is its preference for l…
Unmanned aerial vehicle (UAV) tracking is crucial for autonomous navigation and has broad applications in robotic automation fields. However, reliable UAV tracking remains a challenging task due to various difficulties like frequent occlusion and aspect ratio change. Additionally, most of the existing work mainly focuses on explicit information to improve tracking performance, ignoring potential interframe connections. To address the above issues, this work proposes a novel framework with continuity-aware latent interframe information mining for reliable UAV tracking, i.e., ClimRT. Specificall…
Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is fragile because of two inherent defects, i.e, boundary effect and filter corruption. Some methods by enlarging the search area can mitigate the boundary effect, yet introducing the undesired background distractors. Another approaches can alleviate the temporal degeneration of learned filters by introducing the temporal regularizer, which depends on the assumption that the filers between consecutive frames should be …
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders
S-Lab, Nanyang Technological University（新加坡南洋理工大学S-Lab）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）
University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；Harbin Institute of Technology, Weihai（哈尔滨工业大学（威海））；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
With the advantage of high mobility, Unmanned Aerial Vehicles (UAVs) are used to fuel numerous important applications in computer vision, delivering more efficiency and convenience than surveillance cameras with fixed camera angle, scale and view. However, very limited UAV datasets are proposed, and they focus only on a specific task such as visual tracking or object detection in relatively constrained scenarios. Consequently, it is of great importance to develop an unconstrained UAV benchmark to boost related researches. In this paper, we construct a new UAV benchmark focusing on complex scen…
Tongji University（同济大学）；Hong Kong Polytechnic University（香港理工大学）
Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the prop…
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；School of Electronic and Information Engineering, Foshan University（电子与信息工程学院，佛山大学）
Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST)（机械工程系，乌山国立科学技术研究院）；Agricultural Robotics and Artificial Intelligence, Metafarmers（农业机器人与人工智能，Metafarmers）
