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Vessel Motion Pattern Recognition Based on One-Way Distance and Spectral Clustering Algorithm

机译:基于单向距离和谱聚类算法的船舶运动模式识别

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Identification of vessel motion pattern from large amount of maritime data can help to high level contextual information and improve the effectiveness of surveillance technologies. Vessel routes belonged to certain motion pattern can provide useful information on daily patterns and transit duration. Therefore an approach to identify motion pattern is presented. In paper, the distance similarity matrix of the trajectory dataset was constructed by using the measurement method in trajectory with one-way distance. The regular motion patterns of vessels were extracted from the trajectories spatial distribution learnt by the spectral clustering algorithm. Finally motion patterns of vessel traveling in Qiongzhou strait was extracted using the proposed method. The results showed that the method has high precision on clustering the vessel trajectories and is applicable to identify movement patterns of vessels in maritime areas such as coastal ports, narrow waterway and traffic complex area.
机译:从大量的海事数据中识别船舶运动模式可以帮助获得高水平的背景信息,并提高监视技术的有效性。属于某些运动模式的船只路线可以提供有关日常模式和运输持续时间的有用信息。因此,提出了一种识别运动模式的方法。本文采用单向轨迹的测量方法,建立了轨迹数据集的距离相似度矩阵。从谱聚类算法学习的轨迹空间分布中提取出船只的规律运动模式。最后,利用该方法提取了琼州海峡船舶航行的运动规律。结果表明,该方法对船舶轨迹进行聚类具有较高的精度,可用于识别沿海港口,狭窄航道和交通复杂区域等海域船舶的运动方式。

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