SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas

Rhodes, Bradley J.; Bomberger, Neil A.; Freyman, Todd M.; Kreamer, William; Kirschner, Linda; L’Italien, Adam C.; Mungovan, Wendy; Stauffer, Chris; Stolzar, Lauren; Waxman, Allen M.; Seibert, Michael

doi:10.1117/12.725627

Cited by 12 publications

(4 citation statements)

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“…Considering the advantages presented by vision sensors in terms of target tracking, several systems that integrate cameras as supplementary sensors have been developed to enhance the detection of maritime threats [34][35][36][37][38]. To demonstrate the security improvements achieved, it's essential to examine various deployments of camera-based surveillance systems.…”

Section: Deployments Of Camera-based Surveillance Systems and The Res...mentioning

confidence: 99%

Role and integration of image processing systems for maritime target tracking

Yassir Zardoua,

Bilal Sebbar,

Moussab Chbeine

et al. 2024

World J. Adv. Res. Rev.

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In recent years, maritime traffic has increased, especially in seaborne trade. To ensure safety, security, and environmental protection, various systems have been deployed, often combining data for improved effectiveness. One key application of this combined data is tracking targets at sea, where the Automatic Identification System (AIS) and X-band marine radar are crucial. Recently, there has been growing interest in using visual data from cameras to enhance tracking. This has led to the development of several tracking algorithms based on image processing. While much of the existing literature addresses data fusion, there hasn’t been much focus on why integrating image processing systems is important given the existence of the other systems. In our paper, we aim to analyze these surveillance systems and highlight the benefits of integrating image processing systems. Our main goal is to show how this integration can improve maritime security, offering practical insights into enhancing safety and protection at sea.

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Section: Deployments Of Camera-based Surveillance Systems and The Res...mentioning

confidence: 99%

Role and integration of image processing systems for maritime target tracking

Yassir Zardoua,

Bilal Sebbar,

Moussab Chbeine

et al. 2024

World J. Adv. Res. Rev.

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“…An example of a hybrid system, that is, a combination of both data‐driven and knowledge‐based methods, is SeeCoast (Rhodes, Bomberger, Seibert, & Waxman, ; Rhodes et al, ). SeeCoast extends the detection capability of the learning‐based pattern module based on Rhodes' research (BAE systems) on the problem of learning normal vessel motion patterns (see work on development of such methods in Bomberger et al (), Rhodes, Bomberger, Seibert, and Waxman (), Rhodes, Bomberger, and Zandipour () and Rhodes, Bomberger, Zandipour, Waxman, and Seibert ()).…”

Section: Systemsmentioning

confidence: 99%

Maritime anomaly detection: A review

Riveiro¹,

Pallotta

Vespe

2018

WIREs Data Min & Knowl

109

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The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection. This article is categorized under: Application Areas > Government and Public Sector Algorithmic Development > Spatial and Temporal Data Mining

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“…Then, a coherent track picture was generated according to the AIS data and surface surveillance radars. Based on the track picture, the unsafe, illegal, and threatening vessel activities could be identified using machine learning methods [43]. Other similar systems, such as the satellite-extended-vessel Traffic Service (SEV) system [44] and distributed multi-hypothesis tracking (DMHT) technologybased trackers [45] were also designed, introduced and analyzed in the literature.…”

Section: Interactive Systems For Vessel Anomaly Detectionmentioning

confidence: 99%

Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data

Yan

Wang

2019

Smart Innovation, Systems and Technologies

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Maritime safety and security are gaining increasing concern in recent years. There are a growing number of studies aiming at improving situation awareness in the maritime domain by identifying vessel anomaly behaviors based on the data provided by the Automatic Identification System (AIS). Two types of data-driven methods are most popular in vessel anomaly detection based on AIS data: the statistical methods and machine learning methods. To improve the detection model efficiency and accuracy, hybrid models are formed by combining different types of methods. In order to incorporate expert knowledge, interactive systems are also designed and realized. In this paper, we provide a review of the popular statistical and machine learning models, as well as the hybrid models and interactive systems based on the data-driven methods used for anomaly detection based on AIS data.

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SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas

Cited by 12 publications

References 0 publications

Role and integration of image processing systems for maritime target tracking

Role and integration of image processing systems for maritime target tracking

Maritime anomaly detection: A review

Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data

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