Hyperspectral object tracking has recently emerged as a topic of great interest in the remote sensing community. The hyperspectral image, with its many bands, provides a rich source of material information of an object that can be effectively used for object tracking. While most hyperspectral trackers are based on detection-based techniques, no one has yet attempted to employ YOLO for detecting and tracking the object. This is due to the presence of multiple spectral bands, the scarcity of annotated hyperspectral videos, and YOLO's performance limitation in managing occlusions, and distinguishing object in cluttered backgrounds. Therefore, in this paper, we propose a novel framework called Hy-Tracker, which aims to bridge the gap between hyperspectral data and state-of-the-art object detection methods to leverage the strengths of YOLOv7 for object tracking in hyperspectral videos. Hy-Tracker not only introduces YOLOv7 but also innovatively incorporates a refined tracking module on top of YOLOv7. The tracker refines the initial detections produced by YOLOv7, leading to improved object-tracking performance. Furthermore, we incorporate Kalman-Filter into the tracker, which addresses the challenges posed by scale variation and occlusion. The experimental results on hyperspectral benchmark datasets demonstrate the effectiveness of Hy-Tracker in accurately tracking objects across frames.
Rochester Institute of Technology（罗切斯特理工学院）；Chester F. Carlson Center for Imaging Science（切斯特·F·卡尔森成像科学中心）；School of Mathematical Sciences, Rochester Institute of Technology（罗切斯特理工学院数学科学学院）
Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multi-modal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this study, we apply popular concepts from traditional object tracking, namely (1) Kernelized Correlation Filters (KCF) and (2) Deep Convolutional Neural Network (CNN) features to aerial tracking in hyperspectral domain. We propose the Deep Hyperspectral Kernelized Correlation Filter ba…
BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Key Laboratory of Fractional Signals and Systems（北京分数信号与系统重点实验室）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间生智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德雷塞尔大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程系，密苏里州立大学）
SSF-Net: Spatial-Spectral Fusion Network with Spectral Angle Awareness for Hyperspectral Object Tracking
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；Beijing Institute of Technology, Zhuhai, Guangdong（珠海，广东，北京理工大学）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德克萨斯大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程系，密苏里州立大学）
Hyperspectral Mamba for Hyperspectral Object Tracking
State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University（信息服务网络国家重点实验室，电信工程学院，西安电子科技大学）；The Department of Electronic and Electrical Engineering, the University of Sheffield（电子与电气工程系，谢菲尔德大学）
End-to-End Unmixing with Material Prompts for Hyperspectral Object Tracking
School of Information and Communication Technology, Griffith University, Australia（信息与通信技术学院，格里菲斯大学，澳大利亚）；School of Engineering and Built Environment, Griffith University, Australia（工程与建筑环境学院，格里菲斯大学，澳大利亚）；School of Environment and Science, Griffith University, Australia（环境与科学学院，格里菲斯大学，澳大利亚）
Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e.g., background clutter and rapid changes of target appearance. Alternatively, material information of targets contained in a large amount of bands of hyperspectral images (HSI) is more robust to these difficult conditions. In this paper, we conduct a comprehensive study on how material information can be utilized to boost object tracking from three aspects: benchmark dataset, material feature representation and material based tracking. In terms o…
SUIT: Spatial-Spectral Union-Intersection Interaction Network for Hyperspectral Object Tracking
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；College of Computer Science, Zhejiang University（浙江大学计算机科学学院）
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Meituan Inc（美团公司）；Shanghai Frontiers Science Center of Molecule Intelligent Syntheses（上海分子智能合成前沿科学中心）
Visual object tracking task is constantly gaining importance in several fields of application as traffic monitoring, robotics, and surveillance, to name a few. Dealing with changes in the appearance of the tracked object is paramount to achieve high tracking accuracy, and is usually achieved by continually learning features. Recently, deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative, bypassing the need to retrain the feature extraction in an online fashion. Inspired by the success of residual and dense network…
Tampere University of Technology（坦佩雷理工大学）；University of Ljubljana（卢布尔雅那大学）；Czech Technical University in Prague（布拉格捷克理工大学）
Standard RGB-D trackers treat the target as an inherently 2D structure, which makes modelling appearance changes related even to simple out-of-plane rotation highly challenging. We address this limitation by proposing a novel long-term RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance-enhancing features: (i) generation of accurate spatial support for constrained DCF learning from its 2D proj…
Lost and Found: Overcoming Detector Failures in Online Multi-Object Tracking
Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；CiTIUS, Univ. of Santiago de Compostela（西班牙圣地亚哥-德孔波斯特拉大学CiTIUS研究中心）；Inria Grenoble, Univ. Grenoble Alpes（法国国家信息与自动化研究所格勒诺布尔分部、格勒诺布尔阿尔卑斯大学）
RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）
Neural networks for multi-domain learning empowers an effective combination of information from different domains by sharing and co-learning the parameters. In visual tracking, the emerging features in shared layers of a multi-domain tracker, trained on various sequences, are crucial for tracking in unseen videos. Yet, in a fully shared architecture, some of the emerging features are useful only in a specific domain, reducing the generalization of the learned feature representation. We propose a semi-supervised learning scheme to separate domain-invariant and domain-specific features using adv…
Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration
School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney（电气与数据工程学院，工程与信息技术学院，技术悉尼大学）；Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology（可信自主系统研究所和计算机科学与工程系，南方科技大学）
OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering
School of Electronic Information Engineering, Beihang University（电子信息工程学院，北京航空航天大学）；School of Automation and Intelligent Sensing, Shanghai Jiaotong University（自动化与智能感知学院，上海交通大学）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（计算机科学与工程系，香港科技大学）；College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；School of Artificial Intelligence and Computer Science, Nantong University（人工智能与计算机科学学院，南通大学）；Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；Research Laboratory of Smart Driving and Intelligent Transportation Systems, Southeast University（智能驾驶与智能交通系统研究实验室，东南大学）
How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学物理与数学科学学院）
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）
Beyond RGB: Benchmarking and Enhancing MLLMs for Hyperspectral Image Understanding via Training-Free Reasoning Framework
Sun Yat-sen University（中山大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；China Agricultural University（中国农业大学）；Southwest Jiaotong University（西南交通大学）；Southwest University（西南大学）；National Supercomputing Center in Shenzhen（国家超级计算深圳中心）
HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Mathematics and Statistics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学数学与统计学学院和教育部智能网络与网络安全重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou（广州黄埔 Pazhou 实验室）
TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Quanzhou University of Information Engineering（泉州信息工程学院）；Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学清华-伯克利深圳学院）；Xiamen University（厦门大学）
EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学教育部长春符号计算与知识工程重点实验室）；Yangtze University（扬子大学）
Pyramid Hierarchical Transformer for Hyperspectral Image Classification
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；University of Messina（墨西拿大学）；Shaoxing University（绍兴大学）；Innopolis University（因诺波利斯大学）
We present a deeply integrated method of exploiting low-cost gyroscopes to improve general purpose feature tracking. Most previous methods use gyroscopes to initialize and bound the search for features. In contrast, we use them to regularize the tracking energy function so that they can directly assist in the tracking of ambiguous and poor-quality features. We demonstrate that our simple technique offers significant improvements in performance over conventional template-based tracking methods, and is in fact competitive with more complex and computationally expensive state-of-the-art trackers,…
FARTrack: Fast Autoregressive Visual Tracking with High Performance
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（西安交通大学人机混合增强智能国家重点实验室）；DAMO Academy, Alibaba Group（阿里云达摩院）；National University of Singapore（新加坡国立大学）
Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker
Department of Biomedical Engineering, Stevens Institute of Technology（生物医学工程系，史蒂文斯理工学院）；Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Electrical Engineering, Columbia University in the City of New York（电气工程系，哥伦比亚大学（纽约市））；Nokia Bell Labs（诺基亚贝尔实验室）；Department of Communication & Information Science, University of Alabama（传播与信息科学系，阿拉巴马大学）；Department of Psychology, University of Alabama（心理学系，阿拉巴马大学）；Department of Electrical and Computer Engineering, University of Alabama（电气与计算机工程系，阿拉巴马大学）
MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
College of Engineering and IT, University of Dubai（迪拜大学工程与信息技术学院）；Department of Computer Science and Engineering, Alipurduar Government Engineering and Management College（阿利普杜尔政府工程与管理学院计算机科学与工程系）；Department of Geography, Simon Fraser University（西蒙·弗雷泽大学地理系）
EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
Department of Computer Science, National University of Computer and Emerging Sciences（计算机科学系，国立计算机与新兴科学大学）；Institute of Software Development and Engineering, Innopolis University（软件开发与工程研究所，因诺波利斯大学）；Dipartimento di Matematica e Informatica—MIFT, University of Messina（数学与信息学系—MIFT，墨西拿大学）
Learning to Track Instance from Single Nature Language Description
Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education Guangxi Normal University（教育区块链与智能技术重点实验室，教育部广西师范大学）；University Engineering Research Center of Educational Intelligent Technology Guangxi Normal University（教育智能技术大学工程研究中心，广西师范大学）；University of Southampton（南安普顿大学）
Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Department of Agriculture and Fisheries, Queensland Government（昆士兰州政府农业与渔业部）；School of Information Science and Engineering, Yantai University（烟台大学信息科学与工程学院）
提出融合双向反向 CNN、空间分析模块与注意力式动态特征提取的 SS non-Linear Model，在三个 HSI 数据集上以较低计算成本超越现有方法。
