Graph-Embedding-Based Learning for Robust Object Tracking
文献类型：期刊论文
| 作者 | Zhang, Xiaoqin1,2; Hu, Weiming2 ; Chen, Shengyong3; Maybank, Steve4
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
 |
| 出版日期 | 2014-02-01 |
| 卷号 | 61期号:2页码:1072-1084 |
| 关键词 | Graph embedding object tracking particle filter subspace learning |
| 英文摘要 | Object tracking is viewed as a two-class "one-versus-rest" classification problem, in which the sample distribution of the target over a short period of time is approximately Gaussian while the background samples are often multimodal. Based on these special properties, we propose a graph-embedding-based learning method, in which the topology structures of graphs are carefully designed to reflect the properties of the sample distributions. This method can simultaneously learn the subspace of the target and its local discriminative structure against the background. Moreover, a heuristic negative sample selection scheme is adopted to make the classification more effective. In applications to tracking, the graph-embedding-based learning is incorporated into a Bayesian inference framework cascaded with hierarchical motion estimation, which significantly improves the accuracy and efficiency of the localization. Furthermore, an incremental updating technique for the graphs is developed to capture the changes in both appearance and illumination. Experimental results demonstrate that, compared with the two state-of-the-art methods, the proposed tracking algorithm is more efficient and effective, particularly in dynamically changing and cluttered scenes. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| 研究领域[WOS] | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 关键词[WOS] | VISUAL TRACKING ; MODELS ; SURVEILLANCE ; RECOGNITION ; MOTION ; FILTER ; IMAGE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000323492000044 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3270]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 作者单位 | 1.Wenzhou Univ, Inst Intelligent Syst & Decis, Wenzhou 325035, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China 3.Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Zhejiang, Peoples R China 4.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England |
| 推荐引用方式 GB/T 7714 | Zhang, Xiaoqin,Hu, Weiming,Chen, Shengyong,et al. Graph-Embedding-Based Learning for Robust Object Tracking[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2014,61(2):1072-1084. |
| APA | Zhang, Xiaoqin,Hu, Weiming,Chen, Shengyong,&Maybank, Steve.(2014).Graph-Embedding-Based Learning for Robust Object Tracking.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,61(2),1072-1084. |
| MLA | Zhang, Xiaoqin,et al."Graph-Embedding-Based Learning for Robust Object Tracking".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 61.2(2014):1072-1084. |
入库方式： OAI收割
来源：自动化研究所
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