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A Redefined Codebook Model for Dynamic Backgrounds

  • Vishakha SharmaEmail author
  • Neeta Nain
  • Tapas Badal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

Dynamic background updation is one of the major challenging situation in moving object detection, where we do not have a fix reference background model. The background model maintained needs to be updated as and when moving objects add and leave the background. This paper proposes a redefined codebook model which aims at eliminating the ghost regions left behind when a non-permanent background object starts to move. The background codewords which were routinely deleted from the set of codewords in codebook model are retained in this method while deleting the foreground codewords leading to ghost elimination. This method also reduces memory requirements significantly without effecting object detection, as only the foreground codewords are deleted and not background. The method has been tested for robust detection on various videos with multiple and different kinds of moving backgrounds. Compared to existing multimode modeling techniques our algorithm eliminates the ghost regions left behind when non permanent background objects starts to move. For performance evaluation, we have used similarity measure on video sequences having dynamic backgrounds and compared with three widely used background subtraction algorithms.

Keywords

Motion analysis Background subtraction Object detection Video surveillance 

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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentMalaviya National Institute of TechnologyJaipurIndia

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