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AIS Ship Trajectory Clustering Based on Convolutional Auto-encoder

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1251))

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Abstract

Ship trajectory anomaly detection, route planning, location prediction, collision detection and other issues have become the main research directions in the field of ocean navigation. Ship trajectory clustering is the key to address these problems. By mining the motion patterns of ship trajectory, those similar trajectories are grouped into the same category. Traditional trajectory clustering method usually needs to select the Spatio-temporal trajectory measurement method based on the data volume, computational complexity, noise and other influencing factors. The selection of optimal similarity measure formula needs prior knowledge and extensive experimentation, resulting in computational intensive and time-consuming. In this paper, we propose a ship trajectory motion pattern extraction algorithm based on one-dimensional convolutional auto-encoder without Spatio-temporal trajectory measurement methods. By extracting the low-dimensional representation of the ship’s trajectory, our approach can keep the sequence of trajectory points and reduce the distance calculation bias. The experimental results show that our proposed algorithm has good clustering performance while preserving the main motion characteristics of ship trajectory.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under grant No. 61962017, 61562019, 61662019, the High-level Talents Program of Hainan Province under Grant No. 2019RC088, and grants from State Key Laboratory of Marine Resource Utilization in South China Sea and Key Laboratory of Big Data and Smart Services of Hainan Province.

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Authors and Affiliations

  1. Hainan University, No. 58 Renmin Avenue, Haikou, China

    Taizheng Wang, Chunyang Ye, Hui Zhou, Mingwang Ou & Bo Cheng

  2. State Key Laboratory of Marine Resources Utilization in the South China Sea, Haikou, China

    Taizheng Wang & Chunyang Ye

Authors
  1. Taizheng Wang
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  2. Chunyang Ye
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  3. Hui Zhou
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  4. Mingwang Ou
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  5. Bo Cheng
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Corresponding author

Correspondence to Chunyang Ye .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Wang, T., Ye, C., Zhou, H., Ou, M., Cheng, B. (2021). AIS Ship Trajectory Clustering Based on Convolutional Auto-encoder. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_39

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