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《Pattern Recognition and Artificial Intelligence》 2017-04
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Visual Tracking via Hierarchical Extreme Learning Machine and Local Sparse Model

SUN Rui;ZHANG Dongdong;GAO Jun;School of Computer and Information,Hefei University of Technology;  
To address problems of appearance change and partial occlusion in target tracking,a tracking algorithm is presented via combing hierarchical extreme learning machine( HELM) and adaptive structural local sparse appearance model( ASLSAM). HELM is capable of extracting robust features and fast classification. ASLSAM can improve the tracking accuracy and handle the partial occlusion. Finally,results of both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the tracking process of the proposed algorithm is stable with high tacking precision.
【Fund】: 国家自然科学基金项目(No.61471154)资助~~
【CateGory Index】: TP181;TP391.41
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