Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1)
  • Open Access


    Deep Learning Trackers Review and Challenge

    Yongxiang Gu1, Beijing Chen1, Xu Cheng1,*, Yifeng Zhang2,3, Jingang Shi4

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 23-33, 2019, DOI:10.32604/jihpp.2019.05938

    Abstract Recently, deep learning has achieved great success in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on deep learning. First, we categorize the existing deep learning based trackers into three classes according to network structure, network function and network training. For each categorize, we analyze papers in different categories. Then, we conduct extensive experiments to compare the representative methods on the popular OTB-100, TC-128 and VOT2015 benchmarks. Based on our observations. We conclude that: (1) The usage of the convolutional neural network (CNN) model could significantly improve the tracking performance. (2) The trackers… More >

Displaying 1-10 on page 1 of 1. Per Page