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.

1. 서론
2. 배경
    2.1 클러스터링
    2.2 딥클러스터링
    2.3 궤적이상식별
3. 비정상궤적식별모형
    3.1 입력이미지생성
    3.2 모형의네트워크구조
    3.3 모형의훈련
4. 실험
    4.1 데이터전처리및사전훈련
    4.2 비정상궤적생성
    4.3 사전훈련과클러스터수및임계값설정
    4.4 비정상궤적식별결과및분석
5. 결론
References

• Heon-Jei Park(한남대학교산업공학과) | 박헌제
• Ji Hoon Kyung(한남대학교산업공학과) | 경지훈
• Kyeongtaek Kim(한남대학교산업공학과) | 김경택
• Jae Joon Suh(한밭대학교산업경영공학과) | 서재준