Fast and parameter-light rare behavior detection in maritime trajectories. Issue 5 (September 2020)

• Record Type: Journal Article
• Authors: Wang, Fei; Lei, Yifan; Liu, Zhenguang; Wang, Xun; Ji, Shouling; Tung, Anthony K.H.
• Is Part Of: Information processing & management. Volume 57:Issue 5(2020:Sep.)
• Publication Date: 2020-09
• Subjects: Visual analytics -- Rare behaviour detection -- Trajectory; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102268
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: A scheme cross trajectories, vessel attributes and the movement context for detecting rare behaviors through preprocessing, kNN-based clustering, and verification. Only extremely few anomalies are useful which are rare behaviors. The interactive detection process requires an instant response that is a big challenge for. The more similar trajectories gather in an Area of Interest, the less probability of anomalies they are. The small reachability distance values have limited effect on lrd. Abstract: Rare behaviors indicate important events and situations in maritime surveillance applications. State-of-the-art methods provide many effective solutions to detect anomalous behaviors. Meanwhile, most solutions are parameter-laden and too costly to identify useful rare behaviors with human knowledge in a visual analytics manner. This paper is concerned with a scheme cross trajectories, vessel attributes and the movement context for detecting rare behaviors through preprocessing, kNN-based clustering, and verification. Although the scheme involves several parameters, we demonstrate that they are able to be tackled in thresholds. As a result, a rare behavior factor is the single parameter that affect the detecting results. The proposed scheme is evaluated via a simulated data set for performance and a real life AIS data for effectiveness. Results show that high accuracy to labelled anomalies and useful rare behaviors can be achieved.