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dc.contributor.authorGrabner, Helmut
dc.contributor.authorMatas, Jiří
dc.contributor.authorVan Gool, Luc
dc.contributor.authorCattin, Philippe
dc.date.accessioned2012-06-12T14:03:12Z
dc.date.available2012-06-12T14:03:12Z
dc.date.issued2010-06
dc.identifier.citationHelmut Grabner, Jirí Matas, Luc Van Gool, and Philippe Cattin. Tracking the invisible: Learning where the object might be. In CVPR 2010: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1285-1292, Madison, USA, June 2010. Omnipress.cze
dc.identifier.urihttp://hdl.handle.net/10467/9555
dc.description.abstractObjects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a method to learn supporters which are, be it only temporally, useful for determining the position of the object of interest. Our approach exploits the General Hough Transform strategy. It couples the supporters with the target and naturally distinguishes between strongly and weakly coupled motions. By this, the position of an object can be estimated even when it is not seen directly (e.g., fully occluded or outside of the image region) or when it changes its appearance quickly and significantly. Experiments show substantial improvements in model-free tracking as well as in the tracking of “virtual” points, e.g., in medical applications.eng
dc.language.isocescze
dc.publisherIEEEcze
dc.rightsHelmut Grabner, Jirí Matas, Luc Van Gool, and Philippe Cattin. Tracking the invisible: Learning where the object might be. In CVPR 2010: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1285-1292, Madison, USA, June 2010. Omnipress.eng
dc.titleTracking the Invisible: Learning Where the Object Might becze
dc.typepříspěvek z konference - elektronickýcze
dc.identifier.doi10.1109/CVPR.2010.5539819


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