Target tracking by compressive sensing based on Gaussian differential graph
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Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education,Shcool of Internet of Things Engineering,Jiangnan University,Shcool of Internet of Things Engineering,Jiangnan University,Institute of Intelligent machines,Chinese Academy of Sciences,Shcool of Internet of Things Engineering,Jiangnan University,School of Mechanical Engineering, Xi’an Jiaotong University

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    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.

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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):100~105

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History
  • Received:November 21,2013
  • Revised:December 08,2013
  • Adopted:December 12,2013
  • Online: April 03,2015
  • Published:
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