산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제44권4호
  • /
  • Pages.169-176
  • /
  • 2021
  • /
  • 2005-0461(pISSN)
  • /
  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
권호 표지
DOI QR코드

DOI QR Code

딥 클러스터링을 이용한 비정상 선박 궤적 식별

An Application of Deep Clustering for Abnormal Vessel Trajectory Detection

  • 투고 : 2021.11.18
  • 심사 : 2021.12.20
  • 발행 : 2021.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

키워드

참고문헌

  1. Atluri, G., Karoatne, A., and Kumar, V., Spatio-Temporal Data Mining: A Survey of Problems and Method, ACM Computing Surveys, 2018, Vol. 51, No. 4, Article 83.
  2. Atmosukarto, I., Ghanem, B., and Ahuja, N., Trajectory-based Fisher Kernel Representation for Action Recognition in Videos, 21st International Conference on Pattern Recognition, 2012, pp. 3333-3336.
  3. Aytekin, C., Ni, X., Cricri, F., and Aksu, E., Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representation, arXiv:1802.00187[cs.LG], 2018.
  4. Bian, J., Tian, D., Tang, Y., and Tao, D., A Survey on Trajectory Clustering Analysis, arXiv:1802.06971[cs. CV], 2018.
  5. Dhillon, I.S. and Modha, D.S., Concept Decompositions for Large Sparse Text Data using Clustering, Machine Learning, 2001, Vol. 42, No. 1, pp. 143-175. https://doi.org/10.1023/a:1007612920971
  6. Fu, P., Wang, H., Liu, K., Hu,X., and Zhang, H., Finding Abnormal Vessel Trajectories using Feature Learning, IEEE Access, 2017, Vol. 5, pp. 7898-7909. https://doi.org/10.1109/ACCESS.2017.2698208
  7. Guo, X., Gao, L., Liu, X., and Yin, J., Improved Deep Embedded Clustering with Local Structure Preservation, International Joint Conference on Artificial Intelligence, 2017, pp. 1753-1759.
  8. Guo, X., Liu, X., Zhu, E., and Yin, J., Deep Clustering with Convolutional Autoencoders, International Conference on Neural Information Processing, 2017, pp. 373-382.
  9. Li, S., Liang, M., and Liu, R.W., Vessel Trajectory Similarity Measure based on Deep Convolutional Autoencoder, 2020 5thIEEE International Conference on Big Data Analytics, 2020, pp. 333-338.
  10. Li, X., Zhao, K., Gong, G., Jensen, C.S., and Wei, W., Deep Representation Learning for Trajectory Similarity Computation, IEEE 34th International Conference on Data Engineering, 2018, pp. 617-628.
  11. Liang, M., Liu, R.W., Li, S., Xiao, Z., Liu, X., and Lu, F., An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation, arXiv:2101.03169 [cs.LG], 2021.
  12. Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., and Long, J., A Survey of Clustering with Deep Learning: From the Perspective of Network Architecture, IEEE Access, 2018, Vol. 6, pp. 39501-39514. https://doi.org/10.1109/access.2018.2855437
  13. Naftel, A. and Khalid, S., Motion Trajectory Learning in the DFT-Coefficient Feature Space, Fourth IEEE International Conference on Computer Vision Systems, 2006, pp. 40-47.
  14. Oh, J.-Y., Kim, H.-J., and Park, S.-K., Detection of Ship Movement Anomaly using AIS Data: A Study, Journal of Navigation and Port Research, 2018, Vol. 42, No. 4, pp. 277-282. https://doi.org/10.5394/KINPR.2018.42.4.277
  15. Oh, J.-Y., Kim, H.-J., and Park, S.-K., Development of a Decision Support System based on Autoencoder for Vessel Traffic Service, KSIIE Transactions on Computing Practices, 2018, Vol. 24, No. 12, pp. 642-648. https://doi.org/10.5626/KTCP.2018.24.12.642
  16. Olive, X., Basora, L., Viry, B., and Alligier, R., Deep Trajectory Clustering with Autoencoders, Proceedings of the International Conference on Research in Air Transportation, 2020, pp. 1-8.
  17. Park, J. and Kim, S., Maritime Anomaly Detection Based on VAE-CUSUM Monitoring System, Journal of the Korean Institute of Industrial Engineers, 2020, Vol. 46, No. 4, pp. 432-442. https://doi.org/10.7232/JKIIE.2020.46.4.432
  18. Rezaei, M., Yang, H., and Meinel, K., Deep Neural Network with l2-norm Unit for Brain Lesions Detection, arXiv:1708.05221[cs.CV], 2017.
  19. Santhosh, K. K., Dogra, D. P., Roy, P. P., and Mitra, A., Video Trajectory Classification and Anomaly Detection using Hybrid CNN-VAE, ArXiv: 1812.07203[cs.CV], 2018.
  20. Son, J.-H., Jang, J.-G., Choi, B., and Kim, K., Detection of Abnormal Vessel Trajectories with Convolutional Autoencoder, Journal of Society of Korea Industrial and Systems Engineering, 2020, Vol. 43, No. 4, pp. 190-197. https://doi.org/10.11627/jkise.2020.43.4.190
  21. Taghizadeh, S., Elekes, A., Schaler, M., and Bohm, K., How Meaningful are Similarity in Deep Trajectory Representations?, Information Systems, 2021, Vol. 98, Article 101452.
  22. Wilson, R.C., Hancock, E.R., Pekalska, E., and Duin, R.P.W., Spherical and Hyperbolic Embedding of Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, Vol. 36, No. 11, pp. 2255-2269, 2014. https://doi.org/10.1109/TPAMI.2014.2316836
  23. Xie, J., Girshick, R., and Farhadi, A., Unsupervised Deep Embedding for Clustering Analysis, Proceedings of the 33rd International Conference on Machine Learning, 2016, pp. 478-487.
  24. Zhang, Z., Huang, K., and Tan, T., Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scene, 18th IEEE International Conference on Pattern Recognition, 2006, pp. 1135-1138.