Kamera-basierte Erkennung von Geschwindigkeitsbeschränkungen auf deutschen Stra\en

Nienhüser, Dennis; Ziegenmeyer, Marco; Gumpp, Thomas; Scholl, Kay-Ulrich; Zöllner, J. Marius; Dillmann, Rüdiger

doi:10.1007/978-3-540-74764-2_33

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Previous research in the field of the classification of circular signs [4] showing numbers and pictograms suggests using the gray values of pixels after adequate preprocessing (brightness and size normalization). Consequently we include this in the feature transformation as well.…”

Section: A Feature Space Transformationmentioning

confidence: 99%

“…The knowledge of this regulation can be used to restrict the search area for supplementary signs to relative small regions determined by the position of other traffic signs detected in the image. In this work a detection component for circular [4] and elliptical signs is used to identify search regions for supplementary signs.…”

Section: Supplementary Sign Detectionmentioning

confidence: 99%

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Fast and reliable recognition of supplementary traffic signs

Nienhüser

Gumpp

Zöllner

et al. 2010

2010 IEEE Intelligent Vehicles Symposium

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Supplementary traffic signs are used to alter the meaning of other traffic signs. Assistance systems that recognize traffic signs therefore must also recognize supplementary signs to evaluate their influence on the meaning of detected traffic signs. We propose an algorithm which is able to detect supplementary signs in the vicinity of other signs using a novel rectangle segmentation algorithm. Support vector machines are used for the classification and rejection of other objects. The combination of both components permits to recognize a supplementary sign in less than 40 ms. First quantitative results for a test set with four different supplementary sign types show a very good classification accuracy of more than 96 %.

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Section: A Feature Space Transformationmentioning

confidence: 99%

Section: Supplementary Sign Detectionmentioning

confidence: 99%

Fast and reliable recognition of supplementary traffic signs

Nienhüser

Gumpp

Zöllner

et al. 2010

2010 IEEE Intelligent Vehicles Symposium

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“…Previous work put an emphasis on the recognition of such elements [6], [9], [12], [14], [17]. Based on that work we focus on Dennis Nienhüser, Thomas Gumpp and J. Marius Zöllner are with FZI Forschungszentrum Informatik, Intelligent Systems and Production Engineering, Haid-und-Neu-Straße 10-14, 76131 Karlsruhe, Germany.…”

Section: Introductionmentioning

confidence: 99%

Relevance estimation of traffic elements using Markov logic networks

Nienhüser

Gumpp

Zöllner

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Complex traffic situations e.g. at intersections consist of many traffic participants, traffic elements and relations between them. The behavior of participants is constrained by implicit and explicit traffic rules. We are interested in estimating whether a given traffic element -a traffic sign, a traffic light -is relevant in the current driving situation, i.e. affects the set of possible legal actions. A wide variety of properties influences the relevance. The route to take for example affects which traffic lights are relevant and the current weather situation affects whether a speed limit restricted by a supplementary sign is in effect. We use first-order logic to model such relations and apply reasoning to decide upon the relevance of static traffic elements. The need for perfect information is alleviated with the help of Markov logic networks, reconciling hard decision rules on the one hand and uncertainty intrinsic to the environment perception process on the other hand. The evaluation of twelve intersection scenes shows very promising results for the relevance estimation of traffic lights: Markov logic networks are able to judge whether enough information is available and determine the relevant traffic lights reliably in such cases.

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