A novel approach exploiting properties of convolutional neural networks for vessel movement anomaly detection and classification

Czaplewski, Bartosz; Dzwonkowski, Mariusz

doi:10.1016/j.isatra.2021.02.030

Cited by 13 publications

(1 citation statement)

References 8 publications

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“…Czapelewski et al [80] apply purely synthetic and simplified image data from an aerial view. They intend, however, to extend their approach and experiments on (synthetic) radar data.…”

Section: Data Sourcesmentioning

confidence: 99%

Maritime Anomaly Detection for Vessel Traffic Services: A Survey

Stach

Kinkel

Constapel

et al. 2023

JMSE

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A Vessel Traffic Service (VTS) plays a central role in maritime traffic safety. Regulations are given by the International Maritime Organization (IMO) and Guidelines by the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA). Accordingly, VTS facilities utilize communication and sensor technologies such as an Automatic Identification System (AIS), radar, radio communication and others. Furthermore, VTS operators are motivated to apply Decision Support Tools (DST), since these can reduce workloads and increase safety. A promising type of DST is anomaly detection. This survey presents an overview of state-of-the-art approaches of anomaly detection for the surveillance of maritime traffic. The approaches are characterized in the context of VTS and, thus, most notably, sorted according to utilized communication and sensor technologies, addressed anomaly types and underlying detection techniques. On this basis, current trends as well as open research questions are deduced.

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“…Czapelewski et al [80] apply purely synthetic and simplified image data from an aerial view. They intend, however, to extend their approach and experiments on (synthetic) radar data.…”

Section: Data Sourcesmentioning

confidence: 99%