Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking
	Aziz, Muhammad Ali Abdul 1; Niu, Jianwei 1; Zhao, Xiaoke 1; Li, Xuelong2
刊名	ieee transactions on cybernetics
	2016-04-01
卷号	46 期号:4 页码:945-958
关键词	Computer vision histograms of oriented gradient (HOG) local binary pattern (LBP) machine learning mean shift implanted particle filter
ISSN号	2168-2267
产权排序	2
英文摘要	the use of machine learning approaches for long-term hand tracking poses some major challenges such as attaining robustness to inconsistencies in lighting, scale and object appearances, background clutter, and total object occlusion/disappearance. to address these issues in this paper, we present a robust machine learning approach based on enhanced particle filter trackers. the inherent drawbacks associated with the particle filter approach, i.e., sample degeneration and sample impoverishment, are minimized by infusing the particle filter with the mean shift approach. moreover, to instill our tracker with reacquisition ability, we propose a rotation invariant and efficient detection framework named beta histograms of oriented gradients. our robust appearance model operates on the red, green, blue color histogram and our newly proposed rotation invariant noise compensated local binary patterns descriptor, which is a noise compensated, rotation invariant version of the local binary patterns descriptor. through our experiments, we demonstrate that our proposed hand tracker performs favorably against state-of-the-art algorithms on numerous challenging video sequences of hand postures, and overcomes the largely unsolved problem of redetecting hands after they vanish and reappear into the frame.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	computer science
关键词[WOS]	object tracking ; mean shift ; visual tracking ; sparse representation ; particle filter ; model ; classification ; segmentation
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000372791200007
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/28078]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Aziz, Muhammad Ali Abdul,Niu, Jianwei,Zhao, Xiaoke,et al. Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking[J]. ieee transactions on cybernetics,2016,46(4):945-958.
APA	Aziz, Muhammad Ali Abdul,Niu, Jianwei,Zhao, Xiaoke,&Li, Xuelong.(2016).Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking.ieee transactions on cybernetics,46(4),945-958.
MLA	Aziz, Muhammad Ali Abdul,et al."Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking".ieee transactions on cybernetics 46.4(2016):945-958.