中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Object tracking with collaborative extreme learning machines

文献类型:期刊论文

作者H. P. Kuang and L. Xun
刊名Multimedia Tools and Applications
出版日期2020
卷号79期号:7-8页码:4965-4988
ISSN号1380-7501
DOI10.1007/s11042-018-7135-6
英文摘要We propose a novel collaborative discriminative model based on extreme learning machine (ELM) for object tracking in this paper. In order to represent the object more precisely, we first propose a new collaborative discriminative representation model, which includes both a global discriminative sub-model and a local discriminative sub-model. Different from traditional local representation models, in particular, our local sub-model integrates several classifiers which have structural relations to improve the expression. The global discriminative model represents the appearance comprehensively while the local discriminative sub-model can effectively address occlusions and assist the update. Second, to have better combination of these sub-models, we propose a novel collaboration strategy based on the Kullback-Leibler (KL) distance. The novel strategy can determine the weights of the sub-models adaptively by measuring their KL distances reciprocally. Third, we introduce ELM into tracking and adopt it to build both the global and the local discriminative sub-models simultaneously. Since ELM has a good generalization performance and is robust to the imbalance of the training samples, it is suitable to be used for tracking. Experimental results demonstrate that our method can achieve comparable performance to many state-of-the-art tracking approaches.
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语种英语
源URL[http://ir.ciomp.ac.cn/handle/181722/64738]  
专题中国科学院长春光学精密机械与物理研究所
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H. P. Kuang and L. Xun. Object tracking with collaborative extreme learning machines[J]. Multimedia Tools and Applications,2020,79(7-8):4965-4988.
APA H. P. Kuang and L. Xun.(2020).Object tracking with collaborative extreme learning machines.Multimedia Tools and Applications,79(7-8),4965-4988.
MLA H. P. Kuang and L. Xun."Object tracking with collaborative extreme learning machines".Multimedia Tools and Applications 79.7-8(2020):4965-4988.

入库方式: OAI收割

来源:长春光学精密机械与物理研究所

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