• Journal of Infrared and Millimeter Waves
  • Vol. 34, Issue 1, 100 (2015)
KONG Jun1、2、3、*, JIANG Min1、3, TANG Xiao-Wei1, SUN Yi-Ning3, JIANG Ke1, and WEN Guang-Rui4
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
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    DOI: 10.3724/sp.j.1010.2015.00100 Cite this Article
    KONG Jun, JIANG Min, TANG Xiao-Wei, SUN Yi-Ning, JIANG Ke, WEN Guang-Rui. Target tracking by compressive sensing based on Gaussian differential graph[J]. Journal of Infrared and Millimeter Waves, 2015, 34(1): 100 Copy Citation Text show less

    Abstract

    As traditional target tracking based on compressive sensing has poor robustness in texture change, scale variation and illumination change, a real-time tracking algorithm using compressing sensing based on Gaussian differential graph was proposed. Firstly, Gaussian differential graph is acquired from multi-scale space of image. The features are extracted from the graph and taken as input signals of impressive sensing. Secondly, by compressing, dimension reduction, target neighborhood traversal, parameters update, the optimal search window is estimated. Thirdly, the search window is mapped onto the corresponding original image, and target tracking in the video sequences is finished. Gaussian differential graph had some characteristics such as single-channel, small grayscale range, low value, simple structure, small dimensions, which make the algorithm have strong robustness in scaling, texture and illumination changing. The real-time performance was inherited from the traditional algorithm. Experiments proved that with the proposed algorithm the moving target can be tracked quickly and accurately in a complex environment.
    KONG Jun, JIANG Min, TANG Xiao-Wei, SUN Yi-Ning, JIANG Ke, WEN Guang-Rui. Target tracking by compressive sensing based on Gaussian differential graph[J]. Journal of Infrared and Millimeter Waves, 2015, 34(1): 100
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