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 language | English |
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Pages (from-to) | 5-26 |
Number of pages | 22 |
Journal | Electronic Notes in Theoretical Computer Science |
Volume | 329 |
DOIs | |
State | Published - 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