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Spatiotemporal Models of Human Activity for Robotic Patrolling

  • Tomáš VintrEmail author
  • Kerem Eyisoy
  • Vanda Vintrová
  • Zhi Yan
  • Yassine Ruichek
  • Tomáš Krajník
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11472)

Abstract

We present a method that allows autonomous systems to detect anomalous events in human-populated environments through understating of their structure and how they change over time. We represent the environment by temporary warped space-hypertime continuous models derived from patterns of changes driven by human activities within the observed space. The ability of the method to detect anomalies is evaluated on real-world datasets gathered by robots over the course of several weeks. An earlier version of this approach was already applied to robots that patrolled offices of a global security company (G4S).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Artificial Intelligence Center, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic
  2. 2.Department of Computer Engineering, Faculty of EngineeringMarmara UniversityIstanbulTurkey
  3. 3.Faculty of Informatics and StatisticsUniversity of EconomicsPragueCzech Republic
  4. 4.EPAN Research Group, LE2I-CNRS, University of Technology of Belfort-Montbliard (UTBM)BelfortFrance

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