Abstract
Automatically detecting anomalous vessel behaviour is an extremely crucial problem in intelligent maritime surveillance. In this paper, a deep learning-based unsupervised method is proposed for detecting anomalies in vessel trajectories, operating at both the image and pixel levels. The original trajectory data is converted into a two-dimensional matrix representation to generate a vessel trajectory image. A wasserstein generative adversarial network (WGAN) model is trained on a dataset of normal vessel trajectories, while simultaneously training an encoder to map the trajectory image to a latent space. During anomaly detection, the vessel trajectory image is mapped to a hidden vector by the encoder, which is then used by the generator to reconstruct the input image. The anomaly score is computed based on the residuals between the reconstructed trajectory image and the discriminator’s residuals, enabling image-level anomaly detection. Furthermore, pixel-level anomaly detection is achieved by analyzing the residuals of the reconstructed image pixels to localize the anomalous trajectory. The proposed method is compared to autoencoder (AE) and variational autoencoder (VAE) model, and experimental results demonstrate its superior performance in anomaly detection and pixel-level localization. This method has substantial potential for detecting anomalies in vessel trajectories, as it can detect anomalies in arbitrary waters without prior knowledge, relying solely on training with normal vessel trajectories. This approach significantly reduces the need for human and material resources. Moreover, it provides valuable insights and references for trajectory anomaly detection in other domains, holding both theoretical and practical importance.
L. Weng and M. Liang—Equal contribution.
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References
Kazemi, S., Abghari, S., Lavesson, N., Johnson, H., Ryman, P.: Open data for anomaly detection in maritime surveillance. Exp. Syst. Appl. 40(14), 5719–5729 (2013)
Liang, M., Zhan, Y., Liu, R.W.: MVFFNet: multi-view feature fusion network for imbalanced ship classification. Pattern Recogn. Lett. 151, 26–32 (2021)
Liang, M., Liu, R.W., Li, S., **ao, Z., Liu, X., Lu, F.: An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation. Ocean Eng. 225, 108803 (2021)
Liang, M., Liu, R.W., Zhan, Y., Li, H., Zhu, F., Wang, F.Y.: Fine-grained vessel traffic flow prediction with a spatio-temporal multigraph convolutional network. IEEE Trans. Intell. Transp. Syst. 23(12), 23694–23707 (2022)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Forti, N., d’Afflisio, E., Braca, P., Millefiori, L.M., Willett, P., Carniel, S.: Maritime anomaly detection in a real-world scenario: ever given grounding in the Suez Canal. IEEE Trans. Intell. Transp. Syst. 23(8), 13904–13910 (2021)
Ristic, B., La Scala, B., Morelande, M., Gordon, N.: Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction. In: 2008 11th International Conference on Information Fusion, pp. 1–7. IEEE (2008)
Zhen, R., Riveiro, M., **, Y.: A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Eng. 145, 492–501 (2017)
Li, H., Liu, J., Liu, R.W., **ong, N., Wu, K., Kim, T.: A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis. Sensors 17(8), 1792 (2017)
Pallotta, G., Vespe, M., Bryan, K.: Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy 15(6), 2218–2245 (2013)
Yan, W., Wen, R., Zhang, A.N., Yang, D.: Vessel movement analysis and pattern discovery using density-based clustering approach. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3798–3806. IEEE (2016)
Liu, B., de Souza, E.N., Matwin, S., Sydow, M.: Knowledge-based clustering of ship trajectories using density-based approach. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 603–608. IEEE (2014)
Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1158–1173 (2014)
Lei, P.R.: A framework for anomaly detection in maritime trajectory behavior. Knowl. Inf. Syst. 47(1), 189–214 (2016)
Karataş, G.B., Karagoz, P., Ayran, O.: Trajectory pattern extraction and anomaly detection for maritime vessels. IoT 16, 100436 (2021)
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R., Fablet, R.: GeoTrackNet - a maritime anomaly detector using probabilistic neural network representation of AIS tracks and a contrario detection. IEEE Trans. Intell. Transp. Syst. 23(6), 5655–5667 (2021)
Goodfellow, I.J., et al.: Generative adversarial networks (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. ar**v preprint ar**v:1511.06434 (2015)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Fluss, R., Faraggi, D., Reiser, B.: Estimation of the Youden Index and its associated cutoff point. Biometrical J. J. Math. Meth. Biosci. 47(4), 458–472 (2005)
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Weng, L., Liang, M., Gao, R., Chen, Z.S. (2024). Deep Learning-Empowered Unsupervised Maritime Anomaly Detection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_15
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