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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable.

Details

Title
A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics
Author
Abreu, Fernando H O 1   VIAFID ORCID Logo  ; Soares, Amilcar 2   VIAFID ORCID Logo  ; Paulovich, Fernando V 1   VIAFID ORCID Logo  ; Matwin, Stan 3   VIAFID ORCID Logo 

 Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, Canada; [email protected] (F.H.O.A.); [email protected] (F.V.P.); [email protected] (S.M.) 
 Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada 
 Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, Canada; [email protected] (F.H.O.A.); [email protected] (F.V.P.); [email protected] (S.M.); Institute of Computer Sciences, Polish Academy of Sciences, 01-248 Warsaw, Poland 
First page
412
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
22209964
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2544848252
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.