Water Region Detection Supporting Ship Identification in Port Surveillance

Bao, Xinfeng; Zinger, Svitlana; Wijnhoven, Rob G. J.

doi:10.1007/978-3-642-33140-4_39

Cited by 10 publications

(13 citation statements)

References 8 publications

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“…25 as preprocessing in our region labeling to achieve two objectives: (1) distinguish each region from other objects while preserving the overall characterization of the region itself and (2) perform fast segmentation to support a realtime application in surveillance systems. 35 The basic idea of the graph-based method is that pixels within one region are closer in color space than pixels from different regions. We define the segmentation stage more formally.…”

Section: Context/region Image Segmentationmentioning

confidence: 99%

“…This rate is computed by CRðO; GTÞ ¼ jO ∩ GTj∕jGTj, where we use the manually annotated ground-truth area (GT), and O is the automatically detected area. 44 In order to analyze the performance of our region labeling algorithm, we have compared our results with the method of Bao et al 35 We train and test our gravity model on different color spaces on our dataset. Table 1 illustrates the experimental results of the classification approach based on the gravity model on 30 images of the dataset in three different color spaces: CIE…”

Section: Object-centric Region Labelingmentioning

confidence: 99%

“…We can observe that the gravity model results in a higher CR. In the literature, it has been shown that the unitary-category classification of Bao 35 demonstrates a better performance compared to another state-of-the-art approach. 46 We have extended the unitary-category classification of Bao et al 35 into multicategory classification and applied contextual information as a special feature.…”

Section: Object-centric Region Labelingmentioning

confidence: 99%

“…In the literature, it has been shown that the unitary-category classification of Bao 35 demonstrates a better performance compared to another state-of-the-art approach. 46 We have extended the unitary-category classification of Bao et al 35 into multicategory classification and applied contextual information as a special feature. With our contribution, we have shown that our gravity model for region labeling outperforms the results achieved by Bao et al We have evaluated the performance of our gravity model on the WATERVisie dataset.…”

Section: Object-centric Region Labelingmentioning

confidence: 99%

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Context modeling combined with motion analysis for moving ship detection in port surveillance

Bao

Javanbakhti

Zinger

et al. 2013

J. Electron. Imaging

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Section: Context/region Image Segmentationmentioning

confidence: 99%

Section: Object-centric Region Labelingmentioning

confidence: 99%

Section: Object-centric Region Labelingmentioning

confidence: 99%

Section: Object-centric Region Labelingmentioning

confidence: 99%

See 2 more Smart Citations

Context modeling combined with motion analysis for moving ship detection in port surveillance

Bao

Javanbakhti

Zinger

et al. 2013

J. Electron. Imaging

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“…The basic idea of our graph-based method is that pixels within one region are closer in color space than pixels from different regions. 40 …”

Section: Context/region Segmentationmentioning

confidence: 99%

ViCoMo: visual context modeling for scene understanding in video surveillance

et al. 2013

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract. The use of contextual information can significantly aid scene understanding of surveillance video. Just detecting people and tracking them does not provide sufficient information to detect situations that require operator attention. We propose a proof-of-concept system that uses several sources of contextual information to improve scene understanding in surveillance video. The focus is on two scenarios that represent common video surveillance situations, parking lot surveillance and crowd monitoring. In the first scenario, a pan-tiltzoom (PTZ) camera tracking system is developed for parking lot surveillance. Context is provided by the traffic sign recognition system to localize regular and handicapped parking spot signs as well as license plates. The PTZ algorithm has the ability to selectively detect and track persons based on scene context. In the second scenario, a group analysis algorithm is introduced to detect groups of people. Contextual information is provided by traffic sign recognition and region labeling algorithms and exploited for behavior understanding. In both scenarios, decision engines are used to interpret and classify the output of the subsystems and if necessary raise operator alerts. We show that using context information enables the automated analysis of complicated scenarios that were previously not possible using conventional moving object classification techniques.

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Object Detection and Tracking with Occlusion Handling in Maritime Surveillance-A Review

Banu¹,

Sidram²

2019

Communications in Computer and Information Science

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Water Region Detection Supporting Ship Identification in Port Surveillance

Cited by 10 publications

References 8 publications

Context modeling combined with motion analysis for moving ship detection in port surveillance

Context modeling combined with motion analysis for moving ship detection in port surveillance

ViCoMo: visual context modeling for scene understanding in video surveillance

Object Detection and Tracking with Occlusion Handling in Maritime Surveillance-A Review

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