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Indoor Actions Classification Through Long Short Term Memory Neural Networks

  • Emanuele CipollaEmail author
  • Ignazio Infantino
  • Umberto Maniscalco
  • Giovanni Pilato
  • Filippo Vella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)

Abstract

This work presents a system based on a recurrent deep neural network to classify actions performed in an indoor environment. RGBD and infrared sensors positioned in the rooms are used as data source. The smart environment the user lives in can be adapted to his/her needs.

Keywords

Deep learning Human actions LSTM Indoor activities 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Emanuele Cipolla
    • 1
    Email author
  • Ignazio Infantino
    • 1
  • Umberto Maniscalco
    • 1
  • Giovanni Pilato
    • 1
  • Filippo Vella
    • 1
  1. 1.ICAR, National Research Council of ItalyPalermoItaly

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