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dc.contributor.authorWu, Bing-Feien_US
dc.contributor.authorTseng, Pin-Yien_US
dc.contributor.authorJen, Cheng-Lungen_US
dc.contributor.authorTsou, Tai-Yuen_US
dc.contributor.authorHsiao, Kai-Tseen_US
dc.date.accessioned2015-07-21T08:31:17Z-
dc.date.available2015-07-21T08:31:17Z-
dc.date.issued2013-01-01en_US
dc.identifier.isbn978-1-4799-2384-7; 978-1-4799-2383-0en_US
dc.identifier.issnen_US
dc.identifier.urihttp://hdl.handle.net/11536/125025-
dc.description.abstractIn this work, we present a multiple classifiers system cascades an on-line learning RGB-D appearance model framework in which detection, recognition, and tracking are highly coupled for a wheelchair robot equipped with a Kinect sensor to improve the efficiency of the care assistance and quality of accompanying service. The on-line trained classifiers use the surrounding background as negative examples in the updating which allows the algorithm to choose the most discriminative features between the target and the background, incrementally adjust to the changes in specific tracking environment. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Accordantly, an on-line learning RGB-D appearance model is cascaded to strengthen the human tracking function by dealing with color, depth and position information from the identified caregiver. Consequently, several experiments have been conducted to demonstrate the effectiveness and feasibility in real world environments.en_US
dc.language.isoen_USen_US
dc.subjectWheelchair Roboten_US
dc.subjectOnline Boostingen_US
dc.subjectHaar-like Featureen_US
dc.subjectVariance based Haar-like Featureen_US
dc.subjectFeature Selectionen_US
dc.subjectSemi-supervised Learningen_US
dc.subjectRGB-D Trackingen_US
dc.subjectIncremental Learningen_US
dc.titleAdaptive Online Learning for Human Trackingen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2013 CACS INTERNATIONAL AUTOMATIC CONTROL CONFERENCE (CACS)en_US
dc.citation.spage152en_US
dc.citation.epage157en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000349259200026en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper