DU Feng1,3,WANG Wan-liang1,LI Si-yuan2,ZHANG Zhi3,LIU Zi-yu1
1(School of Computer Science,Zhejiang University of Technology,Hangzhou 310023,China)2(Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China)3(School of Information Engineering,Zhijiang College of Zhejiang University of Technology,Shaoxing 312030,China)
Abstract：In order to improve the performance of the tracking algorithm in the case of fast motion or occlusion,a dynamic weighted hierarchical convolution feature adaptive target tracking algorithm is proposed on the basis of the kernel correlation filter (KCF) framework commonly used in target tracking.After extracting features of different layers of different convolution neural network,different KCF templates were obtained through correlation filter learning.By combining stability and accuracy of each filter and giving dynamic weights to different features,stability and accuracy of each filter,and fusing three templates,the final target position could be determined.The OTB standard database was used to test the performance of the new algorithm under the disturbances such as occlusion,motion blur,fast motion,etc..The results show that the proposed algorithm improves the tracking performance and accuracy,and can flexibly adapt to scenarios with different features.Compared with the classic KCF,the average accuracy is increased by 35.4%,and the average success rate is increased by 33.6%.
2021, Vol. 42 
