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Maritime anomaly detection based on a support vector machine

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Abstract

This paper designs a maritime anomaly detection algorithm based on a support vector machine (SVM) that considers the spatiotemporal and motion features of trajectories. Since trajectories are two-dimensional, it is difficult to present their motion features. To accurately describe trajectory features, a novel trajectory feature extraction method based on statistical theory is proposed in this paper. This method maps trajectories onto a high-dimensional space, which can account for both the spatiotemporal features and motion features of the trajectories. With the proposed feature extraction method, the density-based spatial clustering of applications with noise algorithm is employed to recognize vessel traffic patterns by simultaneously considering the spatiotemporal and motion features. Then, an improved SVM is designed by employing a weighted hybrid kernel function and differential operator to detect anomalous behaviours from recognized vessel traffic patterns that include the spatiotemporal and motion characteristics. Compared with standard SVM, it can adaptively determine the optimal kernel function according to sample set. Finally, a numerical example based on automatic identification system data from the waters off Chengshan Jiao is fulfilled to verify the proposed algorithm effectiveness and accuracy.

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Funding

This research was funded by the National Key Research and Development Program of China, Grant Number 2017YFC0805309, the National Natural Science Foundation of China, Grant Number 71901005, and the Social Science Program of Beijing Municipal Education Committee, Grant Number SM202010011008.

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Authors and Affiliations

  1. 132 Room College of Transportation, Shandong University of Science and Technology, No. 579 Qianwan’gang Road, Qingdao, 266590, China

    Zhaokun Wei

  2. College of Transportation Engineering, Dalian Maritime University, Dalian, China

    Xinlian Xie

  3. Business School, Beijing Technology and Business University, Beijing, China

    Xiaoju Zhang

Authors
  1. Zhaokun Wei
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  2. Xinlian Xie
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  3. Xiaoju Zhang
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Correspondence to Zhaokun Wei.

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Wei, Z., Xie, X. & Zhang, X. Maritime anomaly detection based on a support vector machine. Soft Comput 26, 11553–11566 (2022). https://doi.org/10.1007/s00500-022-07409-w

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