Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (9): 157-162.doi: 10.11896/jsjkx.190800160

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Cascaded Siamese Network Visual Tracking Based on Information Entropy

ZHAO Qin-yan1, LI Zong-min1, LIU Yu-jie1, LI Hua2   

  1. 1 College of Computer & Communication Engineering,China University of Petroleum,Qingdao,Shandong 266580,China
    2 Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2019-08-30 Published:2020-09-10
  • About author:ZHAO Qin-yan,born in 1994,postgra-duate,is a member of China Computer Federation.His main research interests include computer vision,picture processing and machine learning.
    LI Zong-min,born in 1965,professor,is a member of China Computer Federation.His main research interests include computer graphics,picture processing and visualization in scientific computing.
  • Supported by:
    National Natural Science Foundation of China (61379106,61379082,61227802),Shandong Provincial Natural Science Foundation (ZR2013FM036, ZR2015FM011) and Fundamental Research Funds for the Central Universities (18CX06046A).

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Abstract: Visual tracking is an important research direction in the field of computer vision.In view of the problems such as poor robustness of the current algorithms to object appearance changes,this paper proposes a cascaded Siamese network visual trac-king method based on information entropy.Firstly,the deep convolution feature is extracted from the first frame target template and the area to be detected of the current frame by using the Siamese network,and the response map is calculated by correlation.Then,the quality of the response map is evaluated according to the defined information entropy and the average peak coefficient,and for the response map with poor quality,the model factor of convolution feature is updated.Finally,the final response map is used to determine the target position and calculate the optimal scale.The experimental results on VOT2016 and VOT2017 datasets show that the proposed method is superior to other algorithms on the basis of ensuring real-time operation.

Key words: Information entropy, Neural networks, Scale estimation, Visual tracking

CLC Number: 

  • TP391.41
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