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

doi:10.1109/TCYB.2015.2418275

OPT OpenIR > 光谱成像技术研究室

	Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking
	Aziz, Muhammad Ali Abdul 1; Niu, Jianwei 1; Zhao, Xiaoke 1; Li, Xuelong2
作者部门	光学影像学习与分析中心
	2016-04-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	46 期号:4 页码:945-958
产权排序	2
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.
文章类型	Article
关键词	Computer Vision Histograms Of Oriented Gradient (Hog) Local Binary Pattern (Lbp) Machine Learning Mean Shift Implanted Particle Filter
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2418275
收录类别	SCI ; EI
关键词[WOS]	OBJECT TRACKING ; MEAN SHIFT ; VISUAL TRACKING ; SPARSE REPRESENTATION ; PARTICLE FILTER ; MODEL ; CLASSIFICATION ; SEGMENTATION
语种	英语
WOS研究方向	Computer Science
项目资助者	National Natural Science Foundation of China(61170296 ; Research and Development Program(2013BAH35F01) ; Chinese Academy of Sciences(KGZD-EW-T03) ; 973 Program(2013CB035503) ; 61125106 ; 61190125)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000372791200007
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/28078
专题	光谱成像技术研究室
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.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Efficient and Robust（1955KB）	期刊论文	作者接受稿	限制开放	CC BY-NC-SA	请求全文