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Fast Object Detection with Foveated Imaging and Virtual Saccades on Resource Limited Robots

  • Adrian Ratter
  • David Claridge
  • Jayen Ashar
  • Bernhard Hengst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

This paper describes the use of foveated imaging and virtual saccades to identify visual objects using both colour and edge features. Vision processing is a resource hungry operation at the best of times. When the demands require robust, real time performance with a limited embedded processor, the challenge is significant. Our domain of application is the RoboCup Standard Platform League soccer competition using the Aldebaran Nao robot. We describe algorithms that use a combination of down-sampled colour images and high resolution edge detection to identify objects in varying lighting conditions. Optimised to run in real time on autonomous robots, these techniques can potentially be applied in other resource limited domains.

Keywords

Edge Detection Goal Post Ball Detection Saliency Image Foveated Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adrian Ratter
    • 1
  • David Claridge
    • 1
  • Jayen Ashar
    • 1
  • Bernhard Hengst
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of New South Wales, UNSWSydneyAustralia

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