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《Journal of Zhejiang University(Engineering Science)》 2014-12
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Object compressive tracking based on adaptive multi-feature appearance model

LU Wei;XIANG Zhi-yu;YU Hai-bin;LIU Ji-lin;Department of Information Science and Electronic Engineering,Zhejiang University;Zhejiang Provincial Key Laboratory of Information Network Technology;School of Electronic and Information,Hangzhou Dianzi University;  
An adaptive multi-feature modelling method was proposed to resolve the problems of simple feature and inflexible modelling existed in the appearance model of compressive tracking.This method makes the visual representation more abundant and comprehensive through fusing intensity with the Surftype feature which has strong power to describe the detail information like gradient and edge.A two-stage measurement matrix is constructed to measure the multi-dimension features.The Hellinger distance between a feature's distributions of positive and negative samples is computed to analyze the feature's ability of discriminating the object from background.The weights of features in the statistical model can be adjusted adaptively to help the model efficiently explore information that is useful for object tracking,and update according to the changes of object and background.Experimental results show that this adaptive multi-feature modelling method can describe the complex changes of object and background in the real world more accurately,and greatly improve the tracking algorithm's robustness and precision,while holding the high efficiency.
【Fund】: 国家自然科学基金资助项目(61071219 61102132)
【CateGory Index】: TP391.41
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