Deep Adaptive Update of Discriminant KCF for Visual Tracking

  • Xin Ning
  • Weijun LiEmail author
  • Weijuan Tian
  • Xuchi
  • Dongxiaoli
  • Zhangliping
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


In order to solve the challenges of In-plane/Out-of-plane Rotation (IPR/OPR), fast motion (FM) and occlusion (OCC), a new robust visual tracking framework combining an adaptive template update strategy and tracking validity evaluation, named (AU_DKCF) is presented in this paper. Specifically, the proposed appearance discriminant models are firstly used to determine the tracking validity, and then a new adaptive template update strategy is introduced, which provides an efficient update mechanism to distinguish IPR/OPR from FM and OCC states, and furthermore, a new visual tracking framework AU_DKCF is presented, which combines object detection to distinct FM and OCC states. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. Experiment results demonstrate the state-of-the-art performance in tracking accuracy and speed for processing the cases of IPR/OPR, FM and OCC.


Visual tracking Kernelized correlation filters Discriminant model Convolution neural network Object detection 



This work was supported by the National Nature Science Foundation of China (No. 61572458).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xin Ning
    • 1
    • 2
  • Weijun Li
    • 1
    Email author
  • Weijuan Tian
    • 2
  • Xuchi
    • 2
  • Dongxiaoli
    • 1
    • 2
  • Zhangliping
    • 1
    • 2
  1. 1.Institute of SemiconductorsChinese Academy of SciencesBeijingChina
  2. 2.Cognitive Computing Technology Wave Joint LabBeijingChina

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