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.

1. 서 론
2. 관련연구
3. 배경이론
    3.1 클러스터링
    3.2 딥 클러스터링
4. 비정상 궤적 식별 모형
    4.1 입력 이미지 생성
    4.2 모형의네트워크 구조
    4.3 모형의훈련
5. 실험
    5.1 데이터전처리 및 사전 훈련
    5.2 비정상궤적 생성
    5.3 사전 훈련과 클러스터 수 및 임계값 설정
    5.4 비정상궤적 식별 결과 및 분석
6. 결 론
References

• Heon-Jei Park(한남대학교 산업공학과) | 박헌제
• Jun Woo Lee(㈜지디엘시스템) | 이준우
• Ji Hoon Kyung(한남대학교 산업공학과) | 경지훈
• Kyeongtaek Kim(한남대학교 산업공학과) | 김경택 Corresponding Author