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A modular system for global and local abnormal event detection and categorization in videos

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Abstract

This paper presents a modular system for both abnormal event detection and categorization in videos. Complementary normalcy models are built both globally at the image level and locally within pixels blocks. Three features are analyzed: (1) spatio-temporal evolution of binary motion where foreground pixels are detected using an enhanced background subtraction method that keeps track of temporarily static pixels; (2) optical flow, using a robust pyramidal KLT technique; and (3) motion temporal derivatives. At the local level, a normalcy MOG model is built for each block and for each flow feature and is made more compact using PCA. Then, the activity is analyzed qualitatively using a set of compact hybrid histograms embedding both optical flow orientation (or temporal gradient orientation) and foreground statistics. A compact binary signature of maximal size 13 bits is extracted from these different features for event characterization. The performance of the system is illustrated on different datasets of videos recorded on static cameras. The experiments show that the anomalies are well detected even if the method is not dedicated to one of the addressed scenarios.

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Notes

  1. Single instruction multiple data.

  2. http://openmp.org/.

  3. Unusual Crowd Activity Dataset: http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.

  4. http://homepages.inf.ed.ac.uk/rbf/BEHAVE/.

  5. Surveillance imProved sYstem https://itea3.org/project/spy.html.

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Acknowledgments

This research is supported by the European Project ITEA2 SPY Surveillance imProved sYstem https://itea3.org/project/spy.html.

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Authors and Affiliations

  1. Univ. Paris-Sud, Université Paris Saclay, 4 Place Jussieu, 75005, Paris, France

    Ahmed Chamseddine Ben Abdallah

  2. LIMSI, CNRS, Univ. Paris-Sud, Université Paris Saclay, Bât 508, rue John von Neumann, Campus Universitaire, 91405, Orsay, France

    Michèle Gouiffès

  3. Sorbonne Universités, UPMC Univ Paris 06, CNRS UMR 7606, LIP6, 4 Place Jussieu, 75005, Paris, France

    Lionel Lacassagne

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  1. Ahmed Chamseddine Ben Abdallah
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  2. Michèle Gouiffès
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  3. Lionel Lacassagne
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Correspondence to Michèle Gouiffès.

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Abdallah, A.C.B., Gouiffès, M. & Lacassagne, L. A modular system for global and local abnormal event detection and categorization in videos. Machine Vision and Applications 27, 463–481 (2016). https://doi.org/10.1007/s00138-016-0752-z

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  • DOI: https://doi.org/10.1007/s00138-016-0752-z

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