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
Link To Document :
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