A Neuromorphic Approach to Object Detection and Recognition in Airborne Videos with Stabilization

  • Yang Chen
  • Deepak Khosla
  • David Huber
  • Kyungnam Kim
  • Shinko Y. Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

Research has shown that the application of an attention algorithm to the front-end of an object recognition system can provide a boost in performance over extracting regions from an image in an unguided manner. However, when video imagery is taken from a moving platform, attention algorithms such as saliency can lose their potency. In this paper, we show that this loss is due to the motion channels in the saliency algorithm not being able to distinguish object motion from motion caused by platform movement in the videos, and that an object recognition system for such videos can be improved through the application of image stabilization and saliency. We apply this algorithm to airborne video samples from the DARPA VIVID dataset and demonstrate that the combination of stabilization and saliency significantly improves object recognition system performance for both stationary and moving objects.

Keywords

Support Vector Machine Classifier Scale Invariant Feature Transform Saliency Detection Scale Invariant Feature Transform Feature Video Stabilization 
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

  • Yang Chen
    • 1
  • Deepak Khosla
    • 1
  • David Huber
    • 1
  • Kyungnam Kim
    • 1
  • Shinko Y. Cheng
    • 1
  1. 1.HRL LaboratoriesLLCMalibuUSA

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