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

Bao, Xinfeng; Javanbakhti, S Solmaz; Zinger, Svitlana; Wijnhoven, Rob G. J.

doi:10.1117/1.jei.22.4.041114

Cited by 20 publications

(17 citation statements)

References 40 publications

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“…Background modelling techniques only work for fixed camera viewpoints or need time to reinitialize the background model. Related to background modelling, context modelling (Bao et al, 2013) can be used to get a segmentation map consisting of water, vegetation and 'other objects'. Ships are detected by motion analysis of the 'other object' class which lie in the water region.…”

Section: Related Workmentioning

confidence: 99%

Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs

Zwemer

Wijnhoven

With

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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This paper aims at developing a real-time vessel detection and tracking system using surveillance cameras in harbours with the purpose to improve the current Vessel Tracking Systems (VTS) performance. To this end, we introduce a novel maritime dataset, containing 70,513 ships in 48,966 images, covering 10 camera viewpoints indicating real-life ship traffic situations. For detection, a Convolutional Neural Network (CNN) detector is trained, based on the Single Shot Detector (SSD) from literature. This detector is modified and enhanced to support the detection of extreme variations of ship sizes and aspect ratios. The modified SSD detector offers a high detection performance, which is based on explicitly exploiting the aspect-ratio characteristics of the dataset. The performance of the original SSD detector trained on generic object detection datasets (including ships) is significantly lower, showing the added value of a novel surveillance dataset for ships. Due to the robust performance of over 90% detection, the system is able to accurately detect all types of vessels. Hence, the system is considered a suitable complement to conventional radar detection, leading to a better operational picture for the harbour authorities. 1 APPS Project page: https://itea3.org/project/apps.html Zwemer, M., Wijnhoven, R. and With, P. Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs.

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Section: Related Workmentioning

confidence: 99%

Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs

Zwemer

Wijnhoven

With

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…We extract the ROI including the outer part R obj of a candidate and its local background R bg as defined in [3]. We consider only the outer part of an object, since the inner parts of objects are typically appear with the same color.…”

Section: Context-based Motion Saliency Checkingmentioning

confidence: 99%

“…We assume that segments from the same object should have similar motion patterns, which distinguish them from segments belonging to other objects. Therefore, based on spatial adjacency, motion similarity and segment labels, we employ a reliable merging process to form consistent semantic regions [3]. Pixel-level motion is first calculated by optical flow, which is used in measuring motion similarity as well as in the later stages.…”

Section: Region Mergingmentioning

confidence: 99%

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Context-based object-of-interest detection for a generic traffic surveillance analysis system

Bao

Javanbakhti

Zinger

et al. 2014

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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“…We present our novel traffic sign detection and classification algorithm 16 for detecting and classifying various traffic signs that occur in the scenes, in addition to license plates on cars. Additionally, we develop a fast region labeling algorithm 17 to classify pixel regions of the video into broad categories such as sky, vegetation, water, road, construction, and zebra. Finally, a decision engine is designed to interpret the results from all the components and decide if the scene contains suspicious activity.…”

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

Cited by 20 publications

References 40 publications

Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs

Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs

Context-based object-of-interest detection for a generic traffic surveillance analysis system

ViCoMo: visual context modeling for scene understanding in video surveillance

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