Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set

Szpak, Zygmunt L.; Tapamo, Jules-Raymond

doi:10.1016/j.eswa.2010.11.068

Cited by 72 publications

(47 citation statements)

References 28 publications

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“…It could be altered to appear in three-dimensions [18]. The video stream could show just what moves [19], or the camera itself could move to encompass a wider field of view [20].…”

Section: B Computer Visionmentioning

confidence: 99%

Effective threat detection for surveillance

Gelernter

2013

2013 IEEE International Conference on Technologies for Homeland Security (HST)

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Abstract-Surveillance video and protocols are only as effective as the people who are doing the monitoring. Threats might be overlooked because they are rare. Our experiment tested in the security domain what others have found in the medical domain: that repeating a rare event to make it less rare will make it more likely to be remembered, and hence, to be able to be reported. Results of our experiment supported the hypothesis that repeating rare events will make them more likely to be detected. We also looked at the relationship between threat detection performance and age, perceived level of attention, and recording procedure time devoted to each threat. Our specific findings concerning these factors led us to suggest actionable protocols to improve security officers' threat detection performance in video and live surveillance. Airport, border, urban, and other types of security screeners might benefit from these recommendations.

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“…It could be altered to appear in three-dimensions [18]. The video stream could show just what moves [19], or the camera itself could move to encompass a wider field of view [20].…”

Section: B Computer Visionmentioning

confidence: 99%

Effective threat detection for surveillance

Gelernter

2013

2013 IEEE International Conference on Technologies for Homeland Security (HST)

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“…There are several tracking solutions for videosurveillance, and their applicability depends on the scenario: traffic situations (Alvarez et al, 2014), airports (Besada et al, 2005), maritime surveillance (Szpak & Tapamo, 2011), sports events (Kayumbi et al, 2008), places in poor lighting conditions (Wong et al, 2009;Gade & Moeslund, 2014), crowded environments (Zhao & Nevatia, 2004;Wu & Nevatia, 2006;Li et al, 2008Li et al, , 2009Xing et al, 2009;Kuo et al, 2010;Kuo & Nevatia, 2011;Ali & Dailey, 2012;Chau et al, 2014a,b;Badie & Bremont, 2014;Walia & Kapoor, 2014;Guan & Huang, 2015;Zhang et al, 2015), and so forth. However, there is not any realistic approach in related works for conveniently solving the problem of human tracking in a real-time video-surveillance system for shopping malls.…”

Section: Related Workmentioning

confidence: 97%

Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls

Arroyo

Yebes

Bergasa

et al. 2015

Expert Systems with Applications

108

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“…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). In contrast to these applications, the DBN proposed in this study provides a complete framework that considers kinematic behaviour in context of an environment and situation.…”

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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Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set

Cited by 72 publications

References 28 publications

Effective threat detection for surveillance

Effective threat detection for surveillance

Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls

A unified model for context-based behavioural modelling and classification

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