Simulating Surveillance Options for the Canadian North

Lapinski, Anna-Liesa S.; Isenor, Anthony W.; Webb, Sean

doi:10.1017/s0373463316000102

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The data interval is too long for ship tracking in the smart ship era. Also, merchant ships may not be able to access LRIT data as the dataset is confidential (Lapinski et al, 2016; Vespe et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Robust Ship Tracking via Multi-view Learning and Sparse Representation

et al. 2018

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Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos. To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets. First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more. Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively. Finally, our framework is evaluated in four typical maritime surveillance scenarios. The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods.

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Section: Introductionmentioning

confidence: 99%

Robust Ship Tracking via Multi-view Learning and Sparse Representation

et al. 2018

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“…As technologies allowed the monitoring to encompass greater distances, the purpose of the monitoring also changed from an activity focused on port management to one focused on safety and security. With the introduction of technologies that allow vessel monitoring from space-based platforms (Lapinski et al, 2016), the monitoring also took on a global perspective.…”

Section: Introductionmentioning

confidence: 99%

Applying spatial mutual information to AIS data

McArthur

Isenor

2021

J. Navigation

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This paper examines a new interpretation for spatial mutual information based on the mutual information between an attribute value and a spatial random variable. This new interpretation permits the measurement of variations in spatial mutual information over the domain, not only answering the question of whether a spatial dependency exists and the strength of that dependency, but also allowing the identification of where such dependencies exist. Using simulated and real vessel reporting data, the properties of this new interpretation of spatial mutual information are explored. The utility of the technique in detecting spatial boundaries between regions of data having different statistical properties is examined. The technique is shown to successfully identify vessel traffic boundaries, crossing points between traffic lanes, and transitions between regions having differing vessel movement patterns.

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