A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature

Meng, Hongying and Yue, Shigang and Hunter, Andrew and Appiah, Kofi and Hobden, Mervyn and Priestley, Nigel and Hobden, Peter and Pettit, Cy (2009) A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature. In: IEEE International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, USA..

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A modified neural network model for lobula giant movement detector with additional depth movement feature
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A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature
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Abstract

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement.

Item Type:Conference or Workshop Item (Paper)
Additional Information:The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement.
Keywords:Neural networks, Computer vision, Image Processing
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G730 Neural Computing
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:1971
Deposited By:INVALID USER
Deposited On:12 Aug 2009 13:15
Last Modified:13 Mar 2013 08:33

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