Motion detection based on biological correlation model

  • Authors:

  • Bin Sun;Nong Sang;Yuehuan Wang;Qingqing Zheng

  • Affiliations:

  • Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China

  • Venue:
  • ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
  • Year:
  • 2010

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Abstract

Research on the motion perception has received great attention in recent years In this paper, on the basis of existing biological vision achievement, a computer implementation is carried out to examine the performance of the biologically-motivated method for motion detection The proposed implementation is validated in both synthetic and real-world image sequences The experimental comparisons with a representative gradient optical flow solution show that the biological correlation detector has better robustness and anti-noise capability.