{"id":151229,"date":"1999-01-01T00:00:00","date_gmt":"1999-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/tracking-articulated-structures-in-stereo-image-sequences\/"},"modified":"2018-10-16T20:23:34","modified_gmt":"2018-10-17T03:23:34","slug":"tracking-articulated-structures-in-stereo-image-sequences","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/tracking-articulated-structures-in-stereo-image-sequences\/","title":{"rendered":"Tracking Articulated Structures in Stereo Image Sequences"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we present an algorithm for real time 3-D tracking of articulated structures in stereo image sequences. These sequences can be captured by an inexpensive commercially available system that also computes the dense disparity map in real time. In our algorithm, the tracked object is modeled as a set of articulated 3D blobs, each adhering to a Gaussian distribution. Classification of the disparity map pixels into the segments of the articulated object is based on the maximum likelihood principle with an additional mechanism for filling the missing data created by self-occlusions. The articulation constraints are enforced through an Extended Kalman Filter, which can also be used to model the dynamics of the tracked object.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present an algorithm for real time 3-D tracking of articulated structures in stereo image sequences. These sequences can be captured by an inexpensive commercially available system that also computes the dense disparity map in real time. In our algorithm, the tracked object is modeled as a set of articulated 3D blobs, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-151229","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"1999-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"211261","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"santafe.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/santafe.pdf","id":211261,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":211261,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/santafe.pdf"}],"msr-author-ordering":[{"type":"text","value":"Thomas S. 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