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Cluster Analysis of Facial Video Data in Video Surveillance Systems Using Deep Learning

  • Anastasiia D. SokolovaEmail author
  • Andrey V. Savchenko
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 247)

Abstract

In this paper, we propose the approach of structuring information in video surveillance systems by grouping the videos, which contain identical faces. First, the faces are detected in each frame and features of each facial region are extracted at the output of preliminarily trained deep convolution neural networks. Second, the tracks that contain identical faces are grouped using face verification algorithms and hierarchical agglomerative clustering. In the experimental study with the YTF dataset, we examined several ways to aggregate features of individual frame in order to obtain descriptor of the whole video track. It was demonstrated that the most accurate and fast algorithm is the matching of normalized average feature vectors.

Keywords

Organizing video data Deep convolutional neural networks Video surveillance systems 

Notes

Acknowledgements

The work was conducted at Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics and supported by RSF (Russian Science Foundation) grant 14-41-00039.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsLaboratory of Algorithms Technologies for Network AnalysisNizhny NovgorodRussia

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