Motion Detection Using Spiking Neural Network Model

  • QingXiang Wu
  • T. M. McGinnity
  • Liam Maguire
  • Jianyong Cai
  • G. D. Valderrama-Gonzalez
Conference paper

DOI: 10.1007/978-3-540-85984-0_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5227)
Cite this paper as:
Wu Q., McGinnity T.M., Maguire L., Cai J., Valderrama-Gonzalez G.D. (2008) Motion Detection Using Spiking Neural Network Model. In: Huang DS., Wunsch D.C., Levine D.S., Jo KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science, vol 5227. Springer, Berlin, Heidelberg

Abstract

Inspired by the behaviour of the human visual system, a spiking neural network is proposed to detect moving objects in a visual image sequence. The structure and the properties of the network are detailed in this paper. Simulation results show that the network is able to perform motion detection for dynamic visual image sequence. Boundaries of moving objects are extracted from an active neuron group. Using the boundary, a moving object filter is created to take the moving objects from the grey image. The moving object images can be used to recognise moving objects. The moving tracks can be recorded for further analysis of behaviours of moving objects. It is promising to apply this approach to video processing domain and robotic visual systems.

Keywords

Motion detection spiking neural networks visual system 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • QingXiang Wu
    • 1
    • 2
  • T. M. McGinnity
    • 1
  • Liam Maguire
    • 1
  • Jianyong Cai
    • 2
  • G. D. Valderrama-Gonzalez
    • 1
  1. 1.Intelligent Systems Research CentreUniversity of Ulster at Magee Campus, DerryNorthern Ireland, UK
  2. 2.School of School of Physics and OptoElectronic TechnologyFujian Normal UniversityFuzhouChina

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