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dc.contributor.author Woo, Jeong-Woo -
dc.contributor.author Lim, Young-Chul -
dc.contributor.author Lee, Minho -
dc.date.available 2017-07-11T07:01:32Z -
dc.date.created 2017-04-10 -
dc.date.issued 2011-10 -
dc.identifier.issn 0941-0643 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/3434 -
dc.description.abstract This paper proposes a novel dynamic obstacle recognition system combining global feature with local feature to identify vehicles, pedestrians and unknown backgrounds for a driver assistance system. The proposed system consists of two main procedures: a dynamic obstacle detection model to localize an area containing a moving obstacle, and an obstacle identification model, which is a hybrid of global and local information, for recognizing an obstacle with and without occlusion. A dynamic saliency map is used for localizing an area containing a moving obstacle. For the global feature analysis, we propose a modified GIST using orientation features with MAX pooling, which is robust to translation and size variations of an object. Although the global features are a compact way to represent an object and provide a good accuracy for non-occluded objects, they are sensitive to image translation and occlusion. Thus, a local feature-based identification model is also proposed and combined with the global feature. As such, for the obstacle identification problem, the proposed system mainly follows the global feature-based object identification. If the global feature-based model identifies a candidate area as background, the system verifies the area again using the local feature-based model. As a result, the proposed system is able to provide information on both the appearance of obstacles and the class of an obstacle. Experimental results show that the proposed model can successfully detect obstacle candidates and robustly identify obstacles with and without occlusion. © 2010 Springer-Verlag London Limited. -
dc.publisher Springer -
dc.title Dynamic obstacle identification based on global and local features for a driver assistance system -
dc.type Article -
dc.identifier.doi 10.1007/s00521-010-0401-9 -
dc.identifier.wosid 000294910400002 -
dc.identifier.scopusid 2-s2.0-80052795098 -
dc.identifier.bibliographicCitation Neural Computing and Applications, v.20, no.7, pp.925 - 933 -
dc.subject.keywordAuthor Obstacle detection -
dc.subject.keywordAuthor Obstacle identification -
dc.subject.keywordAuthor Dynamic saliency map -
dc.subject.keywordAuthor Modified GIST -
dc.subject.keywordAuthor Orientation feature with MAX pooling -
dc.subject.keywordPlus Automobile Drivers -
dc.subject.keywordPlus Driver Assistance System -
dc.subject.keywordPlus Dynamic Models -
dc.subject.keywordPlus Dynamic Saliency Map -
dc.subject.keywordPlus Feature-Based -
dc.subject.keywordPlus Feature-Based Model -
dc.subject.keywordPlus Feature Extraction -
dc.subject.keywordPlus Global Feature -
dc.subject.keywordPlus Identification Model -
dc.subject.keywordPlus Identification Problem -
dc.subject.keywordPlus Image Translation -
dc.subject.keywordPlus Local Feature -
dc.subject.keywordPlus Local Information -
dc.subject.keywordPlus Modified GIST -
dc.subject.keywordPlus Moving Obstacles -
dc.subject.keywordPlus Object Identification -
dc.subject.keywordPlus Obstacle Detection -
dc.subject.keywordPlus Obstacle Detectors -
dc.subject.keywordPlus Obstacle Identification -
dc.subject.keywordPlus Obstacle Recognition -
dc.subject.keywordPlus Orientation Feature With Max Pooling -
dc.subject.keywordPlus Orientation Features -
dc.subject.keywordPlus RECOGNITION -
dc.subject.keywordPlus Saliency Map -
dc.subject.keywordPlus SELECTIVE ATTENTION MODEL -
dc.subject.keywordPlus Size Variation -
dc.subject.keywordPlus Translation (Languages) -
dc.citation.endPage 933 -
dc.citation.number 7 -
dc.citation.startPage 925 -
dc.citation.title Neural Computing and Applications -
dc.citation.volume 20 -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Computer Science -
dc.relation.journalWebOfScienceCategory Computer Science, Artificial Intelligence -
dc.type.docType Article -
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Division of Automotive Technology 1. Journal Articles

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