SMARTracIn: a concept for spoof resistant tracking of vessels and detection of adverse intentions

Andler, Sten F.; Fredin, Mikael; Gustavsson, Per M.; Laere, Joeri van; Nilsson, Maria; Svenson, Pontus

doi:10.1117/12.818567

Cited by 11 publications

(13 citation statements)

References 3 publications

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“…maritime activities of special significance. The indicators that we have formalised are the synthesis of the outcomes of workshops on user requirements elicitation [1,24], restricting attention to AIS data [20]. Figure 3 displays the hierarchy of our formalisation, i.e.…”

Section: Maritime Situational Indicatorsmentioning

confidence: 99%

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Composite Event Recognition for Maritime Monitoring

Pitsikalis

Artikis

Dreo

et al. 2019

Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems

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Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.

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Section: Maritime Situational Indicatorsmentioning

confidence: 99%

“…The International Maritime Organization (IMO) 1 states that 90% of the global trade is handled by the shipping industry. Maritime monitoring systems support safe shipping by detecting, in real-time, dangerous, suspicious and illegal vessel activities.…”

Section: Introductionmentioning

confidence: 99%

Composite Event Recognition for Maritime Monitoring

Pitsikalis

Artikis

Dreo

et al. 2019

Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems

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“…Another example is cargo anomaly [11], [12], assuming two vessels that transport dangerous cargo approach each other. The littoral rendezvous example is considered as an intentional illegal rendezvous, while the dangerous cargo rendezvous is normally a non-intentional dangerous rendezvous (see Figure 1).…”

Section: Maritime Situation Analysismentioning

confidence: 99%

“…Examples in Figure 1 are: a : A tanker and tugboats (towing or pushing) b : A cargo ship and high-speed boats (smuggling) c : Two cargo ships with dangerous goods (collision) d : Distressed fishing boat and rescue vessels (rescuing) For detection of anomalous rendezvous behavior, we need a comprehensive model of rendezvous situation. The literature only provides case-based examples [4], [5], [10]- [12]. Generally, a rendezvous occurs only when the related entities are "close enough" and engaging in some type of "distinctive interaction", where the meaning of "close enough" and "distinctive interaction" often depend on contextual factors like vessel type, speed, heading, geographic location, time window, the number and type of vessels, and geometric aspects such as the relative size of the rendezvous point or cyclic movement pattern.…”

Section: Maritime Situation Analysismentioning

confidence: 99%

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Maritime Situation Analysis: A Multi-vessel Interaction and Anomaly Detection Framework

Shahir

Glässer

Nalbandyan

et al. 2014

2014 IEEE Joint Intelligence and Security Informatics Conference

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Maritime security is critical for protecting sea lanes, ports, harborsand other critical infrastructure against a broad range of threats and illegal activities like smuggling, human trafficking, piracy and terrorism. Limited resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this paper, we propose a novel situation analysis approach to analyze, detect and differentiate a range of interaction patterns and anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. We analyze vessel interaction scenarios to model common patterns as probabilistic processes in terms of hidden Markov models. To differentiate suspicious activities from unobjectionable behavior, we explore fusion of data and information from observable behavior (geospatial aspects, kinematic features and contextual information) and maritime domain knowledge from diverse sources. Our experimental evaluation using real-world vessel tracking data shows the effectiveness of the approach.

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