Enhancing Automatic Maritime Surveillance Systems With Visual Information

Bloisi, Domenico Daniele; Previtali, Fabio; Pennisi, Andrea; Nardi, Daniele; Fiorini, Michele

doi:10.1109/tits.2016.2591321

Cited by 70 publications

(43 citation statements)

References 23 publications

(28 reference statements)

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“…Similar works focus on the geographical and temporal nature of the data, and very often, those are presented within geovisual analytics, for example, Vatin and Napoli (, ). More specific work on the visualization of outliers appears in Etienne et al () while integrating visual information in existing VTS systems is the focus of Bloisi, Iocchi, Nardi, and Fiorini () and Bloisi, Previtali, Pennisi, Nardi, and Fiorini ().…”

Section: User Aspectsmentioning

confidence: 99%

Maritime anomaly detection: A review

Riveiro¹,

Pallotta

Vespe

2018

WIREs Data Min & Knowl

108

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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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Section: User Aspectsmentioning

confidence: 99%

Maritime anomaly detection: A review

Riveiro¹,

Pallotta

Vespe

2018

WIREs Data Min & Knowl

108

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“…St-Charles method (St. Charles et al, 2015) suggested a new approach using spatio temporal binary similarity descriptors that could handle local changes in the frame due to the camouflage issue. Bloisi et al (2017) developed a modular system for the intelligent surveillance of maritime which fused on the information captured from heterogeneous sources. This work also helps environment protection issues along with dangerous threats coming from the sea such as fishing, illegal smuggling, immigration, piracy and oil spills.…”

Section: Related Workmentioning

confidence: 99%

Adaptive background modelling technique for moving object detection in video under dynamic environment

Yadav¹,

Singh²

2019

IJSTDS

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This work proposes a novel method for detection of motion based object having dynamic scenario in the background. The suggested scheme has a strong potential for real-time applications especially for rafting, river, sea-beach, swimming pools, ponds, etc. Apart from these, this work is very beneficial for surveillance of border, tunnel, traffic in the sea, forest, restricted zones, deep zones, etc. This work develops a statistical p based background subtraction method and implemented in three stages. In the first stage, a background model is developed using few initial frames. In the second stage, this work classifies the foreground using the difference frame and the appropriate threshold value. An automatic threshold value is generated at run-time and updated iteratively. It also reduces the problem of using a constant threshold. In the third stage, morphological filters and connected component based region filtering technique is applied to enhance the detection quality. The extensive experimental result shows more accurate results of proposed method. It also demonstrates better performance against considered state-of-the-art methods.

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“…Radar systems with an Automatic Radar Plotting Aid track unique live targets whose attributes can later be stored for post hoc analysis (Nohara et al, 2008). Satellite images, digital photographs, and video can also be used to supplement vessel and activity detection and tracking (e.g., Taborda and Silva, 2012;Bloisi et al, 2017;Bartholomew et al, 2018;James et al, 2019). The Marine Monitor (M2) is a low-cost proprietary coastal monitoring system developed by ProtectedSeas (2019) that integrates these multiple components including AIS, off-the-shelf marine radar, and a high-definition camera.…”

Section: Introductionmentioning

confidence: 99%

Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

et al. 2020

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Vessel traffic management systems can be employed for environmental management where vessel activity may be of concern. One such location is in San Francisco Bay where a variety of vessel types transit a highly developed urban estuary. We analyzed vessel presence and speed across space and time using vessel data from the Marine Monitor, a vessel tracking system that integrates data from the Automatic Identification System and a marine-radar sensor linked to a high-definition camera. In doing so, we provide data that can inform collision risk to cetaceans who show an increased presence in the Bay and evaluation of the value in incorporating data from multiple sources when observing vessel traffic. We found that ferries traveled the greatest distance of any vessel type. Ferries and other commercial vessels (e.g., cargo and tanker ships and tug boats) traveled consistently in distinct paths while recreational traffic (e.g., motorized recreational craft and sailing vessels) was more dispersed. Large shipping vessels often traveled at speeds greater than 10 kn when transiting the study area, and ferries traveled at speeds greater than 30 kn. We found that distance traveled and speed varied by season for tugs, motorized recreational and sailing vessels. Distance traveled varied across day and night for cargo ships, tugs, and ferries while speed varied between day and night only for ferries. Between weekdays and weekends, distance traveled varied for cargo ships, ferries, and sailing vessels, while speed varied for ferries, motorized recreational craft, and sailing vessels. Radar-detected vessel traffic accounted for 33.9% of the total track distance observed, highlighting the need to include data from multiple vessel tracking systems to fully assess and manage vessel traffic in a densely populated urban estuary.

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Enhancing Automatic Maritime Surveillance Systems With Visual Information

Cited by 70 publications

References 23 publications

Maritime anomaly detection: A review

Maritime anomaly detection: A review

Adaptive background modelling technique for moving object detection in video under dynamic environment

Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

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