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Real-Time Visual Tracking with Compact Shape and Color Feature

查看全文 作　　者：Zhenguo [1]Gao;Shixiong [1]Xia;Yikun [1]Zhang;Rui [1]Yao;Jiaqi [1]Zhao;Qiang [1]Niu;Haifeng [2]Jiang 高影响力作者 机构地区：[1]School of Computer Science and Technology,China University of Mining and Technology,Xuzhou,221116,China;[2]School of Computer Science,The University of Adelaide,Adelaide,SA 5005,Australia高影响力机构 出　　处：《Computers, Materials & Continua》索引2018年第6期,共13页高影响力期刊 基　　金：This work was supported by the National Key Research and Development Plan(No.2016YFC0600908);the National Natural Science Foundation of China(No.61772530,U1610124);Natural Science Foundation of Jiangsu Province of China(No.BK20171192);China Postdoctoral Science Foundation(No.2016T90524,No.2014M551696). 摘　　要：The colour feature is often used in the object tracking.The tracking methods extract the colour features of the object and the background,and distinguish them by a classifier.However,these existing methods simply use the colour information of the target pixels and do not consider the shape feature of the target,so that the description capability of the feature is weak.Moreover,incorporating shape information often leads to large feature dimension,which is not conducive to real-time object tracking.Recently,the emergence of visual tracking methods based on deep learning has also greatly increased the demand for computing resources of the algorithm.In this paper,we propose a real-time visual tracking method with compact shape and colour feature,which forms low dimensional compact shape and colour feature by fusing the shape and colour characteristics of the candidate object region,and reduces the dimensionality of the combined feature through the Hash function.The structural classification function is trained and updated online with dynamic data flow for adapting to the new frames.Further,the classification and prediction of the object are carried out with structured classification function.The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark dataset OTB-100 and OTB-13. 关 键 词：Visual tracking compact feature colour feature structural learning
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