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Object tracking with collaborative extreme learning machines
H. P. Kuang and L. Xun
2020
发表期刊Multimedia Tools and Applications
ISSN1380-7501
卷号79期号:7-8页码:4965-4988
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.
DOI10.1007/s11042-018-7135-6
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收录类别SCI ; EI
语种英语
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文献类型期刊论文
条目标识符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.
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