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

Authors:
Hongying Meng;Shigang Yue;Andrew Hunter;Kofi Appiah;Mervyn Hobden;Nigel Priestley;Peter Hobden;Cy Pettit
Affiliations:
Department of Computing and Informatics, University of Lincoln, UK;Department of Computing and Informatics, University of Lincoln, UK;Department of Computing and Informatics, University of Lincoln, UK;Department of Computing and Informatics, University of Lincoln, UK;E2V Technologies PLC, Lincoln, UK;E2V Technologies PLC, Lincoln, UK;E2V Technologies PLC, Lincoln, UK;E2V Technologies PLC, Lincoln, UK
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
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A modified model for the Lobula Giant Movement Detector and its FPGA implementation

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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.