Real-Time Probabilistic Covariance Tracking With Efficient Model Update

	Real-Time Probabilistic Covariance Tracking With Efficient Model Update
	Wu, Yi 2; Cheng, Jian1 ; Wang, Jinqiao1 ; Lu, Hanqing1 ; Wang, Jun 4; Ling, Haibin 3; Blasch, Erik 5; Bai, Li 6
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2012-05-01
卷号	21 期号:5 页码:2824-2837
文章类型	Article
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.
关键词	Covariance Descriptor Incremental Learning Model Update Particle Filter Riemannian Manifolds Visual Tracking
WOS标题词	Science & Technology ; Technology
关键词[WOS]	VISUAL TRACKING ; RECOGNITION ; SELECTION ; MATRICES ; FEATURES
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000304160800038
引用统计	被引频次：87[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3345
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Wang, Jinqiao
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
通讯作者单位	中国科学院自动化研究所
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.

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