A Deep Learning Approach to Device-Free People Counting from WiFi Signals

  • Iker SobronEmail author
  • Javier Del Ser
  • Iñaki Eizmendi
  • Manuel Velez
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 798)


The last decade has witnessed a progressive interest shown by the community on inferring the presence of people from changes in the signals exchanged by deployed wireless devices. This non-invasive approach finds its rationale in manifold applications where the provision of counting devices to the people expected to traverse the scenario at hand is not affordable nor viable in the practical sense, such as intrusion detection in critical infrastructures. A trend in the literature has focused on modeling this paradigm as a supervised learning problem: a dataset with WiFi traces and their associated number of people is assumed to be available a priori, which permits to learn the pattern between traces and the number of people by a supervised learning algorithm. This paper advances over the state of the art by proposing a novel convolutional neural network that infers such a pattern over space (frequency) and time by rearranging the received I/Q information as a three-dimensional tensor. The proposed layered architecture incorporates further processing elements for a better generalization capability of the overall model. Results are obtained over real WiFi traces and compared to those recently reported over the same dataset for shallow learning models. The superior performance shown by the model proposed in this work paves the way towards exploring the applicability of the latest advances in Deep Learning to this specific case study.


Device-free people counting Internet of Things Convolutional neural network Deep Learning 



This work was supported in part by the Spanish Ministry of Economy and Competitiveness under project 5GnewBROS (TEC2015-66153-P MINECO/FEDER, EU) and by the Basque Government (IT683-13 and the EMAITEK program).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Iker Sobron
    • 1
    Email author
  • Javier Del Ser
    • 2
    • 3
  • Iñaki Eizmendi
    • 1
  • Manuel Velez
    • 1
  1. 1.University of the Basque Country (UPV/EHU)BilbaoSpain
  2. 2.TECNALIA, University of the Basque Country (UPV/EHU)BizkaiaSpain
  3. 3.Basque Center for Applied Mathematics (BCAM)BizkaiaSpain

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