Bag of features with dense sampling for visual tracking

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	Bag of features with dense sampling for visual tracking
	Dai, Pingyang ; Liu, Weisheng ; Wang, Lan ; Li, Cuihua ; Xie, Yi ; Li CH(李翠华)
刊名	http://dx.doi.org/10.12733/jcis8389
	2013-10-15
关键词	Bayesian networks Clustering algorithms Inference engines
英文摘要	The bag-of-feature model has become a state-of-the-art method of visual classification. Visual codebooks can be used to capture image statistical information for object detection and classification, which is extracted from local image patches and based on the quantization of robust appearance descriptors. In this paper, more information of target objects can be captured by dense sampling rather than sparsely sampling. Then a robust visual tracking method is proposed based on dense sampling and bag of features. Firstly, local image patches are densely extracted by sliding windows and represented as invariant descriptors. Secondly, visual codebooks are generated by fast clustering algorithms such as hierarchical k-means. Therefore, the object region and candidate regions are represented by the bag-of-feature model with the learnt codebooks. After that, tracking can operate in a Bayesian inference framework. The bag-of-feature tracking method with dense sampling is adaptive and exible. It works independently in many situations without the complement of existed tracking algorithms. The experiments on various challenging videos demonstrate that the proposed tracker outperforms several state-of-art algorithms. ? 2013 Binary Information Press.
语种	英语
出版者	Binary Information Press
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92638]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Dai, Pingyang,Liu, Weisheng,Wang, Lan,et al. Bag of features with dense sampling for visual tracking[J]. http://dx.doi.org/10.12733/jcis8389,2013.
APA	Dai, Pingyang,Liu, Weisheng,Wang, Lan,Li, Cuihua,Xie, Yi,&李翠华.(2013).Bag of features with dense sampling for visual tracking.http://dx.doi.org/10.12733/jcis8389.
MLA	Dai, Pingyang,et al."Bag of features with dense sampling for visual tracking".http://dx.doi.org/10.12733/jcis8389 (2013).