NDLI: Real-time estimation of hand gestures based on manifold learning from monocular videos

Content Provider	Springer Nature Link
Author	Wang, Yi Luo, ZhongXuan Liu, JunCheng Fan, Xin Li, HaoJie Wu, Yunzhen
Copyright Year	2013
Abstract	Object pose estimation by manifold learning has become a hot research area recently. In this paper, we propose an efficient method that can recover pose and viewpoints for numerous hand gestures from monocular videos based on Locality Preserving Projections. We first select some hand dynamic gestures as primitive hand motions and set a 3D-2D mapping table to relate 3D joint angles of sampling static pose with their projective silhouettes from arbitrary viewpoints. Then the embedding space and explicit mapping function are learnt for every primitive motion. In order to make classification and prediction among those embedding spaces, a Subspace Filtering Algorithm is also proposed which can recognize and recover numerous hand dynamic gestures by the combination of primitive gestures. At last, by using skin color cues and oriented k-Dops, multi-hands can be labeled and tracked separately and accurately. Extensive experimental results demonstrate qualitatively and quantitatively that 3D pose recovery of hands can be achieved by our method robustly and efficiently.
Starting Page	555
Ending Page	574
Page Count	20
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	71
Issue Number	2
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2013-07-11
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Manifold learning Locality preserving projections Gesture recognition Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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