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Human Motion Estimation Based on Low Dimensional Space Incremental Learning
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-16
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
This paper proposes a novel algorithm called low dimensional space incremental learning (LDSIL) to estimate the human motion in 3D from the silhouettes of human motion multiview images.
The proposed algorithm takes the advantage of stochastic extremum memory adaptive searching (SEMAS) and incremental probabilistic dimension reduction model (IPDRM) to collect new high dimensional data samples.
The high dimensional data samples can be selected to update the mapping from low dimensional space to high dimensional space, so that incremental learning can be achieved to estimate human motion from small amount of samples.
Compared with three traditional algorithms, the proposed algorithm can make human motion estimation achieve a good performance in disambiguating silhouettes, overcoming the transient occlusion, and reducing estimation error.
American Psychological Association (APA)
Li, Wanyi& Sun, Jifeng. 2015. Human Motion Estimation Based on Low Dimensional Space Incremental Learning. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-21.
https://search.emarefa.net/detail/BIM-1074417
Modern Language Association (MLA)
Li, Wanyi& Sun, Jifeng. Human Motion Estimation Based on Low Dimensional Space Incremental Learning. Mathematical Problems in Engineering No. 2015 (2015), pp.1-21.
https://search.emarefa.net/detail/BIM-1074417
American Medical Association (AMA)
Li, Wanyi& Sun, Jifeng. Human Motion Estimation Based on Low Dimensional Space Incremental Learning. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-21.
https://search.emarefa.net/detail/BIM-1074417
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074417