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Anomaly Detection in Crowded Scenarios Using Local and Global Gaussian Mixture Models

  • Adrián ToméEmail author
  • Luis Salgado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10617)

Abstract

This paper presents an objective comparison between two approaches for anomaly detection in surveillance scenarios. Gaussian mixture models (GMM) are used in both cases: globally, with a unique model that covers the whole scene; and locally, with one model per spatial location. The two approaches follow a “bottom-up” approach that avoids any object tracking and motion features extracted with a robust optical flow method. Furthermore, we evaluate the contribution of each feature through a statistical tool called Correlation Feature Selection in order to assure the best performance. Evaluation is done in UCSD dataset, concluding that the global model offers better results, outperforming similar anomaly detection approaches.

Keywords

Anomaly detection Gaussian mixture model Robust optical flow Correlation feature selection 

Notes

Acknowledgements

This work has been partially supported by the Ministerio de Economa, Industria y Competitividad of the Spanish Government and the European Regional Development Fund (AIE/FEDER) under projects TEC2013-48453 (MR-UHDTV), RTC-2015-3527-1 (BEGISE) and TEC2016-75981 (IVME).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Grupo de Tratamiento de Imágenes, E.T.S.I. de TelecomunicaciónUniversidad Politécnica de MadridMadridSpain

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