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Object Tracking in Hyperspectral Videos with Convolutional Features and Kernelized Correlation Filter

  • Kun Qian
  • Jun Zhou
  • Fengchao Xiong
  • Huixin Zhou
  • Juan Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)

Abstract

Target tracking in hyperspectral videos is a new research topic. In this paper, a novel method based on convolutional network and Kernelized Correlation Filter (KCF) framework is presented for tracking objects of interest in hyperspectral videos. We extract a set of normalized three-dimensional cubes from the target region as fixed convolution filters which contain spectral information surrounding a target. The feature maps generated by convolutional operations are combined to form a three-dimensional representation of an object, thereby providing effective encoding of local spectral-spatial information. We show that a simple two-layer convolutional networks is sufficient to learn robust representations without the need of offline training with a large dataset. In the tracking step, KCF is adopted to distinguish targets from neighboring environment. Experimental results demonstrate that the proposed method performs well on sample hyperspectral videos, and outperforms several state-of-the-art methods tested on grayscale and color videos in the same scene.

Keywords

Target tracking Hyperspectral video Correlation filter Convolutional networks 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kun Qian
    • 1
  • Jun Zhou
    • 2
  • Fengchao Xiong
    • 3
  • Huixin Zhou
    • 1
  • Juan Du
    • 1
  1. 1.Lab of Optoelectronic Imaging and Image ProcessingXidian UniversityXi’anPeople’s Republic of China
  2. 2.School of Information and Communication TechnologyGriffith UniversityBrisbaneAustralia
  3. 3.College of Computer ScienceZhejiang UniversityHangzhouPeople’s Republic of China

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