Motion Detection Using Spiking Neural Network Model

Qingxiang Wu; TM McGinnity; LP Maguire; Jianyong Cai; German Valderrama

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

Motion Detection Using Spiking Neural Network Model

Qingxiang Wu, TM McGinnity, LP Maguire, Jianyong Cai, German Valderrama

Faculty Of Computing, Eng. & Built Env.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

506 Downloads (Pure)

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 by the spiking neural network. 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.

Original language	English
Title of host publication	Unknown Host Publication
Place of Publication	LNAI: Lecture Notes In Artificial Intelligence
Publisher	Springer
Pages	76-83
Number of pages	8
ISBN (Print)	978-3-540-85983-3
DOIs	https://doi.org/10.1007/978-3-540-85984-0_10
Publication status	Published (in print/issue) - 18 Sept 2008
Event	ICIC '08 Proceedings of the 4th international conference on Intelligent Computing - with Aspects of Artificial Intelligence - Shanghai, China Duration: 18 Sept 2008 → …

Conference

Conference	ICIC '08 Proceedings of the 4th international conference on Intelligent Computing - with Aspects of Artificial Intelligence
Period	18/09/08 → …

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

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title = "Motion Detection Using Spiking Neural Network Model",

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 by the spiking neural network. 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.",

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Wu, Q, McGinnity, TM , Maguire, LP, Cai, J & Valderrama, G 2008, Motion Detection Using Spiking Neural Network Model. in Unknown Host Publication. Springer, LNAI: Lecture Notes In Artificial Intelligence, pp. 76-83, ICIC '08 Proceedings of the 4th international conference on Intelligent Computing - with Aspects of Artificial Intelligence, 18/09/08. https://doi.org/10.1007/978-3-540-85984-0_10

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AU - Wu, Qingxiang

AU - McGinnity, TM

AU - Maguire, LP

AU - Cai, Jianyong

AU - Valderrama, German

PY - 2008/9/18

Y1 - 2008/9/18

N2 - 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 by the spiking neural network. 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.

AB - 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 by the spiking neural network. 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.

U2 - 10.1007/978-3-540-85984-0_10

DO - 10.1007/978-3-540-85984-0_10

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ER -

Motion Detection Using Spiking Neural Network Model

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