Characterisation of a complex maritime scene using Fourier space analysis to identify small craft

Sanderson, James G.; Teal, M.K.; Ellis, Tim

doi:10.1049/cp:19990435

Cited by 11 publications

(8 citation statements)

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“…An approach to detect pirates through satellite communication monitoring has been proposed [54]. Other approaches intend to detect pirate vessels by classifying small craft in imagery [55], [56].…”

Section: Background and Related Workmentioning

confidence: 99%

Maritime piracy situation modelling with dynamic Bayesian networks

Dabrowski

Villiers

2015

Information Fusion

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A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets.

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Section: Background and Related Workmentioning

confidence: 99%

Maritime piracy situation modelling with dynamic Bayesian networks

Dabrowski

Villiers

2015

Information Fusion

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“…A satellite communication monitoring system for detecting maritime pirates has been proposed (Baldini et al, 2010). Other methods that detect pirate vessels by small craft classification in various forms of imagery have been proposed (Teutsch and Kruger, 2010), (Sanderson et al, 1999). A computer vision method of identifying and tracking vessels, such as pirate vessels, is presented in (Szpak and Tapamo, 2011).…”

Section: Maritime Piracy Applicationsmentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

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A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.

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“…As the ocean, constantly filled with moving waves, is prone to http://jivp.eurasipjournals.com/content/2013/1/42 producing erroneous detection with methods that detect moving objects, some authors choose to characterise it and label pixels that do not match this characterisation as objects of interest. Sanderson et al [3] implement an algorithm that does this using frequency information. Smith and Teal [19] implement a similar approach using a histogram-based descriptor of the appearance of the sea.…”

Section: Traditional Approachmentioning

confidence: 99%

“…While radar systems have been extensively used in maritime environments, these generally require large, metallic targets. Modern pirates favour small, fast rigid inflatable boats that are mainly non-metallic and thus difficult to detect [3]. While the solution to this would seem to be the use of manual detection using dedicated crew members on board, the small number present at any given time makes this unfeasible.…”

Section: Introductionmentioning

confidence: 99%

Detection and tracking of moving objects in a maritime environment using level set with shape priors

Frost

Tapamo

2013

J Image Video Proc

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Over the years, maritime surveillance has become increasingly important due to the recurrence of piracy. While surveillance has traditionally been a manual task using crew members in lookout positions on parts of the ship, much work is being done to automate this task using digital cameras coupled with a computer that uses image processing techniques that intelligently track object in the maritime environment. One such technique is level set segmentation which evolves a contour to objects of interest in a given image. This method works well but gives incorrect segmentation results when a target object is corrupted in the image. This paper explores the possibility of factoring in prior knowledge of a ship's shape into level set segmentation to improve results, a concept that is unaddressed in maritime surveillance problem. It is shown that the developed video tracking system outperforms level set-based systems that do not use prior shape knowledge, working well even where these systems fail.

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Characterisation of a complex maritime scene using Fourier space analysis to identify small craft

Cited by 11 publications

References 0 publications

Maritime piracy situation modelling with dynamic Bayesian networks

Maritime piracy situation modelling with dynamic Bayesian networks

A unified model for context-based behavioural modelling and classification

Detection and tracking of moving objects in a maritime environment using level set with shape priors

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