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Robust Hand Gesture Recognition Using Machine Learning with Positive and Negative Samples
Zhu H.-M.; Pun C.-M.; Lin C.
2013-07-08
Source PublicationJournal of Computers (Finland)
ISSN1796203X
Volume8Issue:7Pages:1831-1835
AbstractHuman action understanding is one of the most attractive research areas in computer vision. In this paper, we focus on a subset of human action which is the gesture performed by hand motion. To track the trajectory of motion, we adopt efficient and robust object detection and tracking schemes, which used Randomized Forest and Online Appearance model. Multiple hand templates are leaned using positive and negative samples (P-N learning). According to robust hand tracking and trajectory enhancement, we recognize the gesture with the baseline SVM tool. The effectiveness of the approach is demonstrated by experiments on the dataset of hand signed digit gestures. © 2013 ACADEMY PUBLISHER.
KeywordClassification Motion tracking P-N learning Trajectory
DOI10.4304/jcp.8.7.1831-1835
URLView the original
Language英語
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Document TypeJournal article
CollectionUniversity of Macau
AffiliationUniversidade de Macau
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GB/T 7714
Zhu H.-M.,Pun C.-M.,Lin C.. Robust Hand Gesture Recognition Using Machine Learning with Positive and Negative Samples[J]. Journal of Computers (Finland),2013,8(7):1831-1835.
APA Zhu H.-M.,Pun C.-M.,&Lin C..(2013).Robust Hand Gesture Recognition Using Machine Learning with Positive and Negative Samples.Journal of Computers (Finland),8(7),1831-1835.
MLA Zhu H.-M.,et al."Robust Hand Gesture Recognition Using Machine Learning with Positive and Negative Samples".Journal of Computers (Finland) 8.7(2013):1831-1835.
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