Event cameras are ideal for object tracking applications due to their ability to capture fast-moving objects while mitigating latency and data redundancy. Existing event-based clustering and feature tracking approaches for surveillance and object detection work well in the majority of cases, but fall short in a maritime environment. Our application of maritime vessel detection and tracking requires a process that can identify features and output a confidence score representing the likelihood that the feature was produced by a vessel, which may trigger a subsequent alert or activate a classification system. However, the maritime environment presents unique challenges such as the tendency of waves to produce the majority of events, demanding the majority of computational processing and producing false positive detections. By filtering redundant events and analyzing the movement of each event cluster, we can identify and track vessels while ignoring shorter lived and erratic features such as those produced by waves.
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
Wuhan University of Technology（武汉理工大学）；Chongqing Research Institute, Wuhan University of Technology（武汉理工大学重庆研究院）；Nanyang Technological University（南洋理工大学）
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
University of Tuebingen（图宾根大学）；University of Ljubljana（卢布尔雅那大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；University of South-Eastern Norway（东南挪威大学）；University of Lisbon（里斯本大学）；Kitware（Kitware公司）；University of Jyväskylä（于韦斯屈莱大学）；The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）；Fraunhofer IOSB（弗劳恩霍夫光学、信息技术与电子学研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）；Helmut Schmidt University（赫尔穆特·施密特大学）；University of Seoul（首尔大学）；University of Cagliari（卡利亚里大学）；Granular AI（Granular AI公司）；TU Kaiserslautern（凯泽斯劳滕工业大学）
Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance
Wuhan University of Technology（武汉理工大学）；University of Aberdeen（阿伯丁大学）；Guangdong University of Technology（广东工业大学）；The Hong Kong Polytechnic University（香港理工大学）
DMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms
College of Information Science and Engineering, Ocean University of China（信息科学与工程学院，中国海洋大学）；Marine Engineering College, Dalian Maritime University（航海工程学院，大连海事大学）；CSSC Haiwei Tech Co., Ltd（CSSC海威科技有限公司）；College of Artificial Intelligence, Dalian Maritime University（人工智能学院，大连海事大学）；College of Marine Electrical Engineering, Dalian Maritime University（海洋电气工程学院，大连海事大学）
Event-based Object Detection and Tracking for Space Situational Awareness
International Centre for Neuromorphic Systems（国际神经形态系统中心）；The MARCS Institute for Brain, Behaviour, and Development（MARCS大脑、行为与发展研究所）；Western Sydney University（西悉尼大学）
Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways
Wuhan University of Technology（武汉理工大学）；National Engineering Research Center for Water Transport Safety（国家水运安全工程技术研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
Anhui University（安徽大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Optics and Electronics, CAS（中国科学院光电技术研究所）；Beijing Institute of Technology（北京理工大学）；Peng Cheng Laboratory（鹏城实验室）；Peking University（北京大学）
Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition
Biomedical Engineering and Neuroscience Program, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University（西悉尼大学MARCS脑、行为与发展研究所生物医学工程与神经科学项目）
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
University of Electronic Science and Technology of China（电子科学与技术大学）；Shenzhen Loop Area Institute（深圳河套学院）；Liaoning Technical University（辽宁技术大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset
School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；School of Electronic and Information Engineering, Anhui University（电子与信息工程学院，安徽大学）
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光电技术研究所）；Anhui University（安徽大学）；Beijing Institute of Technology（北京理工大学）；Peng Cheng Laboratory（鹏城实验室）；Peking University（北京大学）
TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
School of Automation, Southeast University（东南大学自动化学院）；Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich（慕尼黑技术大学机器人与人工智能与实时系统教授职位）；Department of Computer Science and Technology, Tongji University（同济大学计算机科学与技术系）
