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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 169 - 176 (8page)
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초록· 키워드
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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.
목차
등록된 정보가 없습니다.
참고문헌 (24)
참고문헌 신청
Atluri, G. / 2018 / Spatio-Temporal Data Mining: A Survey of Problems and Method / ACM Computing Surveys 51(4):83
Atmosukarto, I / 2012 / Trajectorybased Fisher Kernel Representation for Action Recognition in Videos / 21st International Conference on Pattern Recognition :3333~3336
Aytekin, C. / 2018 / Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representation / arXiv : 1802. 00187[cs. LG]
Bian, J. / 2018 / A Survey on Trajectory Clustering Analysis / arXiv : 1802. 06971[cs. CV]
Dhillon, I. S. / 2001 / Concept Decompositions for Large Sparse Text Data using Clustering / Machine Learning 42(1):143~175
Atmosukarto, I / 2012 / Trajectorybased Fisher Kernel Representation for Action Recognition in Videos / 21st International Conference on Pattern Recognition :3333~3336
Aytekin, C. / 2018 / Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representation / arXiv : 1802. 00187[cs. LG]
Bian, J. / 2018 / A Survey on Trajectory Clustering Analysis / arXiv : 1802. 06971[cs. CV]
Dhillon, I. S. / 2001 / Concept Decompositions for Large Sparse Text Data using Clustering / Machine Learning 42(1):143~175
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