딥 클러스터링을 이용한 비정상 선박 궤적 식별

• 영문명: An Application of Deep Clustering for Abnormal Vessel Trajectory Detection
• 발행기관: 한국산업경영시스템학회
• 저자명: 박헌제(Heon-Jei Park) 이준우(Jun Woo Lee) 경지훈(Ji Hoon Kyung) 김경택(Kyeongtaek Kim)
• 간행물 정보: 『산업경영시스템학회지』제44권 제4호, 169~176쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.12.31

영문 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.

목차

Ⅰ. 서론

Ⅱ. 관련연구

Ⅲ. 배경이론

Ⅳ. 비정상 궤적 식별 모형

Ⅴ. 실험

Ⅵ. 결 론

References

키워드

Deep Learning Anomaly Detection Vessel Trajectory