Real-Time Probabilistic Covariance Tracking With Efficient Model Update
文献类型：期刊论文
| 作者 | Wu, Yi2; Cheng, Jian1 ; Wang, Jinqiao1; Lu, Hanqing1; Wang, Jun4; Ling, Haibin3; Blasch, Erik5; Bai, Li6
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2012-05-01 |
| 卷号 | 21期号:5页码:2824-2837 |
| 关键词 | Covariance descriptor incremental learning model update particle filter Riemannian manifolds visual tracking |
| 英文摘要 | The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | VISUAL TRACKING ; RECOGNITION ; SELECTION ; MATRICES ; FEATURES |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000304160800038 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3345]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Nanjing Univ Informat Sci & Technol, Sch Informat & Control Engn, Nanjing 210044, Jiangsu, Peoples R China 3.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA 4.Nanjing Univ Informat Sci & Technol, Network Ctr, Nanjing 210044, Jiangsu, Peoples R China 5.USAF, Res Lab, AFRL RYAA, Wright Patterson AFB, OH 45433 USA 6.Temple Univ, Dept Elect & Comp Engn, Philadelphia, PA 19122 USA |
| 推荐引用方式 GB/T 7714 | Wu, Yi,Cheng, Jian,Wang, Jinqiao,et al. Real-Time Probabilistic Covariance Tracking With Efficient Model Update[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2012,21(5):2824-2837. |
| APA | Wu, Yi.,Cheng, Jian.,Wang, Jinqiao.,Lu, Hanqing.,Wang, Jun.,...&Bai, Li.(2012).Real-Time Probabilistic Covariance Tracking With Efficient Model Update.IEEE TRANSACTIONS ON IMAGE PROCESSING,21(5),2824-2837. |
| MLA | Wu, Yi,et al."Real-Time Probabilistic Covariance Tracking With Efficient Model Update".IEEE TRANSACTIONS ON IMAGE PROCESSING 21.5(2012):2824-2837. |
入库方式： OAI收割
来源：自动化研究所
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