Visual tracking and learning using speeded up robust features

	Visual tracking and learning using speeded up robust features
	Li J. Y.; Wang Y. L.; Wang Y. J.
	2012
发表期刊	Pattern Recognition Letters
ISSN	0167-8655
卷号	33 期号:16 页码:2094-2101
摘要	A speeded up robust features (SURF) based optical flow algorithm is presented for visual tracking in real scenarios. SURF construct invariant features to correspond the blobs of interest across frames. Meanwhile, new feature-based optical flow algorithm is used to compute the warp matrix of a region centered on SURF key points. Furthermore, on-line visual learning for long-term tracking is performed using incremental object subspace method, which includes the correct update of the sample mean and appearance model. The proposed SURF based tracking and learning method contributes measurably to improving overall tracking performance. Experimental work demonstrates that the proposed strategy improves the performance of the classical optical flow algorithms in complicated real scenarios. (c) 2012 Elsevier B.V. All rights reserved.
收录类别	SCI ; EI
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/34113
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Li J. Y.,Wang Y. L.,Wang Y. J.. Visual tracking and learning using speeded up robust features[J]. Pattern Recognition Letters,2012,33(16):2094-2101.
APA	Li J. Y.,Wang Y. L.,&Wang Y. J..(2012).Visual tracking and learning using speeded up robust features.Pattern Recognition Letters,33(16),2094-2101.
MLA	Li J. Y.,et al."Visual tracking and learning using speeded up robust features".Pattern Recognition Letters 33.16(2012):2094-2101.