Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6): 110-117.doi: 10.11896/jsjkx.200800212

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Temporal Consistency Preserving Multi-Mask Sparse Deep Representation for Visual Tracking

GUO Wen1, YIN Tong-ling1, ZHANG Tian-zhu2, XU Chang-sheng3   

  1. 1 School of Information and Electronic Engineering,Shandong Technology and Business University, Yantai,Shandong 264009,China
    2 School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China
    3 National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2020-08-30 Revised:2020-10-21 Online:2021-06-15 Published:2021-06-03
  • About author:GUO Wen,born in 1978,Ph.D,associate professor,is a member of China Computer Federation.His main interests include computer vision and multimedia computing.
  • Supported by:
    National Natural Science Foundation of China(62072286,61876100,61572296) and Shandong Provincial Natural Science Foundation of China(ZR2015FL020).

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Abstract: Building a model that can not only fully consider the discriminability of the object appearance,but also keep the temporal consistency of the features in tracking process is the key to solve the tracking problem.In order to improve the discrimination of feature representation and alleviate the degradation of feature in tracking process,a novel temporal consistency preserving multi-mask sparse deep representation method for visual tracking is proposed in the paper.Firstly,multi-task sparse deep expression learning method is constructed by using different feature attributes of deep convolution features on different layers to fully explore the correlation of multi-source information.Secondly,the temporal consistency constrained regularization term is constructed by the residual of relevant frames,which can compensate for the degradation of tracking process features and improve the temporal consistency of tracking features.Numerous experimental results on Benchmark show that this algorithm has better tracking effectiveness and stability than the current state-of-the-art methods in complex background,fast motion and other situations.

Key words: Deep convolutional feature, Multi-mask learning, Temporal consistency, Visual tracking

CLC Number: 

  • TP311
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