Online semi-supervised compressive coding for robust visual tracking

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	Online semi-supervised compressive coding for robust visual tracking
	Chen, Si ; Li, Shaozi ; Su, Songzhi ; Cao, Donglin ; Ji, Rongrong ; Li SZ(李绍滋) ; Su SZ(苏松志) ; Cao DL(曹冬林) ; Ji RR(纪荣嵘)
刊名	http://dx.doi.org/10.1016/j.jvcir.2014.01.010
	2014
关键词	RESTRICTED ISOMETRY PROPERTY OBJECT TRACKING MODEL
英文摘要	National Nature Science Foundation of China [61373076, 61202143, 61201359]; Fundamental Research Funds for the Central Universities [20131 21026, 2011121052]; 985 Project of Xiamen University; Natural Science Foundation of Fujian Province [2013105100, 2010101345, 2011101367]; Key Projects Fund of Science and Technology in Xiamen [3502Z20123017]; Research Fund for the Doctoral Program of Higher Education of China [201101211120024]; Special Fund for Developing Shenzhen's Strategic Emerging Industries [JCYJ20120614164600201]; Hunan Provincial Natural Science Foundation [12112040]; Hunan Province Research Foundation of Education Committee [09A046]; In this paper we propose an online semi-supervised compressive coding algorithm, termed SCC, for robust visual tracking. The first contribution of this work is a novel adaptive compressive sensing based appearance model, which adopts the weighted random projection to exploit both local and discriminative information of the object. The second contribution is a semi-supervised coding technique for online sample labeling, which iteratively updates the distributions of positive and negative samples during tracking. Under such a circumstance, the pseudo-labels of unlabeled samples from the current frame are predicted according to the local smoothness regularizer and the similarity between the prior and the current model. To effectively track the object, a discriminative classifier is online updated by using the unlabeled samples with pseudo-labels in the weighted compressed domain. Experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art tracking methods on challenging video sequences. (C) 2014 Elsevier Inc. All rights reserved.
语种	英语
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92673]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Chen, Si,Li, Shaozi,Su, Songzhi,et al. Online semi-supervised compressive coding for robust visual tracking[J]. http://dx.doi.org/10.1016/j.jvcir.2014.01.010,2014.
APA	Chen, Si.,Li, Shaozi.,Su, Songzhi.,Cao, Donglin.,Ji, Rongrong.,...&纪荣嵘.(2014).Online semi-supervised compressive coding for robust visual tracking.http://dx.doi.org/10.1016/j.jvcir.2014.01.010.
MLA	Chen, Si,et al."Online semi-supervised compressive coding for robust visual tracking".http://dx.doi.org/10.1016/j.jvcir.2014.01.010 (2014).