Robust detection, classification and positioning of traffic signs from street-level panoramic images for inventory purposes

Hazelhoff, Lykele; Creusen, Ivo

doi:10.1109/wacv.2012.6163006

Cited by 23 publications

(18 citation statements)

References 11 publications

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“…2) have become important for the making of a sign inventory to support road maintenance and safety. 16 Similar developments can be seen for computer vision applications in the car industry and the exploitation of pictorial city databases on the Internet. An example is the commercial in-car traffic sign detection system by Mobileye 18 that can detect various common traffic signs in real time while driving.…”

Section: Traffic Sign and License Plate Detectionmentioning

confidence: 70%

“…This class of unreliable signs is used for after checking, whereas the reliable sign class is immediately accepted to save efforts. 16 This reliable class is fully automatically classified and has a high accuracy.…”

Section: Traffic Sign Detection and Classificationmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

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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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Section: Traffic Sign and License Plate Detectionmentioning

confidence: 70%

Section: Traffic Sign Detection and Classificationmentioning

confidence: 99%

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confidence: 99%

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ViCoMo: visual context modeling for scene understanding in video surveillance

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“…This work is part of a larger automatic traffic sign surveying system described recently [1,2]. In addition to detection, the system needs to perform classification of the specific traffic sign, and the 3D-position of the sign needs to be determined with triangulation techniques.…”

Section: Introductionmentioning

confidence: 99%

Color transformation for improved traffic sign detection

Creusen¹,

Hazelhoff²

2012

2012 19th IEEE International Conference on Image Processing

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“…Lykele Hazelhoff and Ivo Creusen etc report their works which different from others. They detect present signs in street-level panoramic images and the signs also need to be positioned besides the detection and classification [8] . Jung-Guk Park etc use machine learning algorithms to detect traffic sign, and scale-space to handle the different scale of traffic signs.…”

Section: Bintroductionmentioning

confidence: 99%

Traffic Sign Detection Based on Biologically Visual Mechanism

Zhu

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

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Robust detection, classification and positioning of traffic signs from street-level panoramic images for inventory purposes

Cited by 23 publications

References 11 publications

ViCoMo: visual context modeling for scene understanding in video surveillance

ViCoMo: visual context modeling for scene understanding in video surveillance

Color transformation for improved traffic sign detection

Traffic Sign Detection Based on Biologically Visual Mechanism

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