Motion Detection Using Spiking Neural Network Model

Authors:
Qingxiang Wu;T. M. Mcginnity;Liam Maguire;Jianyong Cai;G. D. Valderrama-Gonzalez
Affiliations:
Intelligent Systems Research Centre, University of Ulster at Magee Campus, Derry, Northern Ireland, UK BT48 7JL and School of School of Physics and OptoElectronic Technology, Fujian Normal Univers ...;Intelligent Systems Research Centre, University of Ulster at Magee Campus, Derry, Northern Ireland, UK BT48 7JL;Intelligent Systems Research Centre, University of Ulster at Magee Campus, Derry, Northern Ireland, UK BT48 7JL;School of School of Physics and OptoElectronic Technology, Fujian Normal University, Fuzhou, China 350007;Intelligent Systems Research Centre, University of Ulster at Magee Campus, Derry, Northern Ireland, UK BT48 7JL
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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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.