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dc.contributor.authorBuzan, Danen_US
dc.date.accessioned2011-10-20T04:19:16Z
dc.date.available2011-10-20T04:19:16Z
dc.date.issued2004-04-23
dc.identifier.urihttps://hdl.handle.net/2144/1544
dc.description.abstractThis technical report presents a combined solution for two problems, one: tracking objects in 3D space and estimating their trajectories and second: computing the similarity between previously estimated trajectories and clustering them using the similarities that we just computed. For the first part, trajectories are estimated using an EKF formulation that will provide the 3D trajectory up to a constant. To improve accuracy, when occlusions appear, multiple hypotheses are followed. For the second problem we compute the distances between trajectories using a similarity based on LCSS formulation. Similarities are computed between projections of trajectories on coordinate axes. Finally we group trajectories together based on previously computed distances, using a clustering algorithm. To check the validity of our approach, several experiments using real data were performed.en_US
dc.language.isoen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2004-016
dc.titleRobust Tracking of Human Motionen_US
dc.typeTechnical Reporten_US


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