Target detection in thermal-visible surveillance based on multiple-valued immune network
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28th Institute of China Electronics Technology Group Corporation,School of Electronic Information,Wuhan University,State Key Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University

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    Abstract:

    Two fuzzy adaptive resonance neural networks were utilized to build the background models of thermal and visible components. According to the multiple-valued immune network model, a series of immune response strategies were designed to cooperate B cell with T cell to build the interactive model, which takes the infrared background model as B cell and the visible background model as T cell. With the interactive model, the targets are detected according to the degree of fuzzy match between pixels and models. Experimental results show that the F1 measurement of the proposed approach is up to 96.4%. It is able to complement information between thermal and visible components effectively. The method is capable of detecting targets in complex scenes effectively.

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CHEN Bing-Wen, WANG Wen-Wei, QIN Qian-Qing. Target detection in thermal-visible surveillance based on multiple-valued immune network[J]. Journal of Infrared and Millimeter Waves,2014,33(6):654~659

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History
  • Received:June 04,2013
  • Revised:October 07,2014
  • Adopted:October 24,2013
  • Online: November 27,2014
  • Published: