• 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