Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (9): 271-276.doi: 10.11896/j.issn.1002-137X.2019.09.041

• Graphics,Image & Pattern Recognition • Previous Articles     Next Articles

Long-term Object Tracking Based on Kernelized Correlation Filter and Hierarchical Convolution Features

CHEN Wei1, LI Jue-long2, XING Jian-chun1, YANG Qi-liang1, ZHOU Qi-zhen1   

  1. (National Defense Engineering College,Army Engineering University of PLA,Nanjing 210007,China)1;
    (Research Center of Coastal Defense Engineering,Beijing 100841,China)2
  • Received:2018-08-15 Online:2019-09-15 Published:2019-09-02

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Abstract: Aiming at the problems such as deformation,scale variation,target occlusion,and out of sight during long-term object tracking,this paper proposed a long-term object tracking algorithm based on kernelized correlation filter and hierarchical convolution feature.Firstly,the pre-trained convolution neural network is applied to extract the hierarchical convolution feature,so as to train correlation filter and estimate location.Then the target scale pyramid is constructed to estimate scale.In order to prevent tracking failure caused by target occlusion and tanget leaving the field of vision,an online support vector machine is trained for target re-detection to achieve long-term tracking.Experimental results on long-term object tracking dataset show that the accuracy of the proposed algorithm is 7%,15%,17%,21% and 50% higher than that of HCF,LCT,DSST,KCF and TLD.

Key words: Hierarchical convolution features, Kernelized correlation filter, Long-term object tracking, Support vector machine

CLC Number: 

  • TP391.41
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