The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state of the art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers.
Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach
Tecnologico de Monterrey（蒙特雷理工学院）；Universidad de las Américas Puebla（普埃布拉美洲大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；CIATEQ A.C.（CIATEQ先进技术中心）
SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking
College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University（三峡大学湖北省建筑质量检测装备工程技术研究中心）；Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University（三峡大学水电工程智能视觉监测湖北省重点实验室）
Infrared target tracking based on proximal robust principal component analysis method
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子工程与光电技术学院）；The 28th Research Institute of China Electronics Technology Corporation（中国电子科技集团公司第二十八研究所）
SDTracker: Synthetic Data Based Multi-Object Tracking
SenseTime Research（商汤科技研究院）；Shanghai Jiao Tong University（上海交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Forestry University（北京林业大学）
A Comparative Study of Object Trackers for Infrared Flying Bird Tracking
Beihang University（北京航空航天大学）；Temple University（天普大学）；US Air Force Research Laboratory（美国空军研究实验室）；China Academy of Civil Aviation Science and Technology（中国民航科学技术研究院）
Coordinate-Aware Thermal Infrared Tracking Via Natural Language Modeling
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China（电子科技大学光电科学与工程学院）；Yibin Institute, University of Electronic Science and Technology of China（电子科技大学宜宾研究院）；NO.208 research institute of China Ordnance Industries（中国兵器工业第208研究所）
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）
In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking
School of Cyber Science and Engineering, Qufu Normal University（曲阜师范大学网络与网络安全学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Integrated Circuits, Harbin Institute of Technology（哈尔滨工业大学集成电路学院）；Faculty of Software and Information Science, Iwate Prefectural University（岩手县立大学软件与信息科学学部）
SynthVerse: A Large-Scale Diverse Synthetic Dataset for Point Tracking
University of Liverpool（利物浦大学）；Zhejiang University（浙江大学）；Durham University（杜伦大学）；Beihang University（北京航空航天大学）；Duke Kunshan University（杜克昆山大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Xi’an Jiaotong University（西安交通大学）；Tsinghua University（清华大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
RAMCT: Novel Region-adaptive Multi-channel Tracker with Iterative Tikhonov Regularization for Thermal Infrared Tracking
College of Computer and Information Technology, China Three Gorges University（中国三峡大学计算机与信息学院）；Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University（中国三峡大学湖北省建筑质量检测装备工程技术研究中心）；Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University（中国三峡大学水电工程智能视觉监测湖北省重点实验室）
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking
College of Computer and Information Technology, China Three Gorges University（中国三峡大学计算机与信息学院）；Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University（中国三峡大学湖北省建筑质量检测装备工程技术研究中心）；Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University（中国三峡大学水电工程智能视觉监测湖北省重点实验室）
DCFG: Diverse Cross-Channel Fine-Grained Feature Learning and Progressive Fusion Siamese Tracker for Thermal Infrared Target Tracking
College of Computer and Information Technology, China Three Gorges University（中国三峡大学计算机与信息学院）；Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, China Three Gorges University（中国三峡大学湖北省建筑质量检测装备工程技术研究中心）；Wuhan College（武汉学院）
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Anhui University（安徽大学）；Southern University of Science and Technology（南方科技大学）
Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)
Intuitive Surgical Inc.（直观外科公司）；ImFusion GmbH（ImFusion有限公司）；Technical University of Munich（慕尼黑工业大学）；University of British Columbia（英属哥伦比亚大学）；Johns Hopkins University（约翰·霍普金斯大学）；Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）；Hamburg University of Technology（汉堡工业大学）；SustAInLivWork Center of Excellence（可持续生活工作卓越中心）；Graduate School of Informatics, Nagoya University（名古屋大学信息科学研究生院）；Information Technology Center, Nagoya University（名古屋大学信息技术中心）；Department of Information Science, Aichi Institute of Technology（爱知工业大学信息科学系）；Research Center for Medical Bigdata, National Institute of Informatics（国立信息学研究所医学大数据研究中心）；Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresde（德累斯顿国家肿瘤疾病中心（NCT）转化外科肿瘤学系，NCT/UCC德累斯顿，由德国癌症研究中心、医学院和卡尔·古斯塔夫·卡鲁斯大学医院、德累斯顿工业大学合作成立）
A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
Center for Aerospace Research (CfAR), University of Victoria（维多利亚大学航空航天研究中心）；Advanced Control and Intelligent Systems (ACIS) Lab, University of Victoria（维多利亚大学先进控制与智能系统实验室）
