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计算机系统应用英文版:2021,30(8):232-236
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基于轨迹图像特征匹配的渔船轨迹相似度计算和轨迹分类
(1.青岛科技大学 信息科学技术学院, 青岛 266061;2.温州大学 计算机与人工智能学院, 温州 325035)
Similarity Calculation and Trajectory Classification of Fishing Boats Based on Trajectory Image Feature Matching
(1.College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China;2.College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China)
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Received:November 20, 2020    Revised:December 21, 2020
中文摘要: AIS (Automatic Identification System)是一种船舶的自动识别系统, 可以提供船舶的时间戳、经纬度、航向角度、速度等数据信息. 本文针对船舶航行轨迹多维度的特点以及对船舶轨迹预测的精确度和实时性的需求, 提出了一种基于图像检测和匹配的计算轨迹相似度的方法. 该方法首先将所有渔船轨迹数据进行可视化, 再通过ORB (Oriented FAST and Rotated BRIEF)算法和BF (Brute-Force)匹配来计算轨迹图片相似度用于划分渔船轨迹类型. 实验结果显示, 通过该计算相似度的方法具有精度高、易实现的特点, 与传统计算方法相比, 其在处理轨迹数据的效率和速度更具有优越性.
中文关键词: AIS数据  ORB  BF  轨迹图片相似度
Abstract:The Automatic Identification System (AIS) can provide the time stamp, latitude and longitude, heading angle, speed, and other data information of ships. In light of multi-dimensional ship trajectories and the demand for accuracy and timeliness in trajectory prediction, this study proposes a calculation method for trajectory similarity based on image detection and matching. To be specific, the trajectory data of all fishing boats are first visualized, and then the Oriented FAST and Rotated BRIEF (ORB) algorithm and Brute-Force (BF) matching are used to calculate the similarity between trajectory pictures for classifying fishing boat trajectories. The experimental results show that the proposed method has high accuracy and can be easily implemented and is superior to the traditional methods in the processing efficiency and speed of trajectory data.
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基金项目:浙江省基础公益研究计划(LGN20F020001)
引用文本:
徐文进,解钦,黄海广.基于轨迹图像特征匹配的渔船轨迹相似度计算和轨迹分类.计算机系统应用,2021,30(8):232-236
XU Wen-Jin,XIE Qin,HUANG Hai-Guang.Similarity Calculation and Trajectory Classification of Fishing Boats Based on Trajectory Image Feature Matching.COMPUTER SYSTEMS APPLICATIONS,2021,30(8):232-236