Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107949
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dc.contributor.authorMilan, A.en
dc.contributor.authorLeal-Taixé, L.en
dc.contributor.authorSchindler, K.en
dc.contributor.authorReid, I.en
dc.date.issued2015en
dc.identifier.citationProceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015 / vol.07-12-June-2015, pp.5397-5406en
dc.identifier.isbn9781467369640en
dc.identifier.issn1063-6919en
dc.identifier.urihttp://hdl.handle.net/2440/107949-
dc.description.abstractTracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these "dots" over time. An obvious shortcoming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, realworld videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.en
dc.description.statementofresponsibilityAnton Milan, Laura Leal-Taixé, Konrad Schindler, Ian Reiden
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognitionen
dc.rights© 2015 IEEEen
dc.subjectTrajectory, target tracking, image edge detection, image segmentation, shape, detectors, optimizationen
dc.titleJoint tracking and segmentation of multiple targetsen
dc.typeConference paperen
dc.identifier.rmid0030043862en
dc.contributor.conference2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (07 Jun 2015 - 12 Jun 2015 : Boston, MA)en
dc.identifier.doi10.1109/CVPR.2015.7299178en
dc.relation.granthttp://purl.org/au-research/grants/arc/FL130100102en
dc.identifier.pubid192059-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS06en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidReid, I. [0000-0001-7790-6423]en
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