Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking
文献类型：期刊论文
| 作者 | Aziz, Muhammad Ali Abdul1; Niu, Jianwei1; Zhao, Xiaoke1; Li, Xuelong2
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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