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Background Modeling Using Deep-Variational Autoencoder

  • Midhula VijayanEmail author
  • R. Mohan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

A foreground segmentation algorithm using the variational autoencoder with skip architecture is presented in this paper. The deep-variational autoencoder network is trained using the target frame and its ground truth image. The variational encoder network constructs non-handcrafted feature sets, and the decoder-network transforms the feature sets into the segmented binary map. Moreover, the variational autoencoder with skip architecture accurately segment the moving objects. The skip architecture used to combine the fine and the coarse scale feature information. Experiments conducted on ‘changedetection.net-2014 (CDnet-2014)’ dataset show that the variational autoencoder based algorithm produces significant results when compared with the classical background modeling methods.

Keywords

Foreground segmentation Convolutional Neural Network (CNN) Background-modeling Skip architecture Variational autoencoder (VAE) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Institute of TechnologyTiruchirappalliIndia

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