Anomalous Human Behavior Detection Using a Network of RGB-D Sensors

  • Nicola Mosca
  • Vito Renò
  • Roberto Marani
  • Massimiliano Nitti
  • Fabio Martino
  • Tiziana D’Orazio
  • Ettore Stella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10188)

Abstract

The detection of anomalous behaviors of people in indoor environments is an important topic in surveillance applications, especially when low cost solutions are necessary in contexts such as long corridors of public buildings, where standard cameras with long camera view would be either ineffective or costly to implement. This paper proposes a network of low cost RGB-D sensors with no overlapping fields-of-view, capable of identifying anomalous behaviors with respect a pre-learned normal one. A 3D trajectory analysis is carried out by comparing three different classifiers (SVM, neural networks and k-nearest neighbors). The results on real experiments prove the effectiveness of the proposed approach both in terms of performances and of real time application.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nicola Mosca
    • 1
  • Vito Renò
    • 1
  • Roberto Marani
    • 1
  • Massimiliano Nitti
    • 1
  • Fabio Martino
    • 1
  • Tiziana D’Orazio
    • 1
  • Ettore Stella
    • 1
  1. 1.National Research Council of Italy, Institute of Intelligent Systems for AutomationBariItaly

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