Fast Face Detection in Violent Video Scenes

V. E. Machaca Arceda, K. M. Fernández Fabián, P. C. Laguna Laura, J. J. Rivera Tito, J. C. Gutiérrez Cáceres

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

In this work we aim to detect faces in violence scenes, in order to help the security control. We used the Violent Flow (ViF) descriptor with Horn-Schunck proposed in [V. Machaca Arceda and K. Fernańdez Fabián and J.C. Gutiérrez, Real Time Violence Detection in Video, “IET Conference Proceedings”, Institution of Engineering and Technology. (2016)] for violence scenes detection at first stage. Then we applied the non-adaptive interpolation super resolution algorithm to improve the video quality and finally we fire a Kanade-Lucas-Tomasi (KLT) face detector. In order to get a very low time processing, we paralleled the super resolution and face detector algorithms with CUDA. For the experiments we used the Boss Dataset and also we built a violence dataset, taking scenes from surveillance cameras. We have promising results detecting faces in this environment, because of the benefits of our proposal.

Original languageEnglish
Pages (from-to)5-26
Number of pages22
JournalElectronic Notes in Theoretical Computer Science
Volume329
DOIs
StatePublished - 9 Dec 2016

Bibliographical note

Publisher Copyright:
© 2016 The Author(s)

Keywords

  • Face detection
  • GPU parallel computing
  • Horn-Schunck
  • Lower computational time
  • Optic Flow
  • Resolution enhancement
  • Super-Resolution
  • ViF
  • Video

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