DocumentCode
2713924
Title
A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature
Author
Meng, Hongying ; Yue, Shigang ; Hunter, Andrew ; Appiah, Kofi ; Hobden, Mervyn ; Priestley, Nigel ; Hobden, Peter ; Pettit, Cy
Author_Institution
Dept. of Comput. & Inf., Univ. of Lincoln, Lincoln, UK
fYear
2009
fDate
14-19 June 2009
Firstpage
2078
Lastpage
2083
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.
Keywords
collision avoidance; neural nets; object detection; depth movement feature; firing rate; lobula giant movement detector; locust nervous system; modified neural network model; object detection; robots; vehicles; visual collision avoidance systems; wide-field visual neuron; Biological neural networks; Collision avoidance; Detectors; Nervous system; Neural networks; Neurons; Object detection; Robots; Testing; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location
Atlanta, GA
ISSN
1098-7576
Print_ISBN
978-1-4244-3548-7
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2009.5179023
Filename
5179023
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