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dc.contributor.authorChoi, Changhyunen_US
dc.contributor.authorChristensen, Henrik I.en_US
dc.date.accessioned2013-04-26T19:59:15Z
dc.date.available2013-04-26T19:59:15Z
dc.date.issued2012-03-07
dc.identifier.citationChanghyun Choi, Henrik I. Christensen, “Robust 3D Visual Tracking Using Particle Filtering on the Special Euclidean Group: A Combined Approach of Keypoint and Edge Features,” International Journal of Robotics Research (IJRR), 31, 4, 498–519 ( Apr. 2012)en_US
dc.identifier.issn0278-3649
dc.identifier.urihttp://hdl.handle.net/1853/46855
dc.description© The Author(s) 2012en_US
dc.descriptionDOI: 10.1177/0278364912437213en_US
dc.description.abstractWe present a 3D model-based visual tracking approach using edge and keypoint features in a particle filtering framework. Recently, particle filtering based approaches have been proposed to integrate multiple pose hypotheses and have shown good performance, but most of the work has made an assumption that an initial pose is given. To ameliorate this limitation, we employ keypoint features for initialization of the filter. Given 2D-3D keypoint correspondences, we randomly choose a set of minimum correspondences to calculate a set of possible pose hypotheses. Based on the inlier ratio of correspondences, the set of poses are drawn to initialize particles. After the initialization, edge points are employed to estimate inter-frame motions. While we follow a standard edge-based tracking, we perform a refinement process to improve the edge correspondences between sampled model edge points and image edge points. For better tracking performance, we employ a first order autoregressive state dynamics, which propagates particles more effectively than Gaussian random walk models. The proposed system re-initializes particles by itself when the tracked object goes out of the field of view or is occluded. The robustness and accuracy of our approach is demonstrated using comparative experiments on synthetic and real image sequences.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectVisual trackingen_US
dc.subjectEdge-based trackingen_US
dc.subjectKeypoint featuresen_US
dc.subjectParticle filteringen_US
dc.titleRobust 3D Visual Tracking Using Particle Filtering on the Special Euclidean Group: A Combined Approach of Keypoint and Edge Featuresen_US
dc.typeArticleen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.publisher.originalSAGE Publicationsen_US
dc.identifier.doi10.1177/0278364912437213


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