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Statistical Features-Based Violence Detection in Surveillance Videos

  • K. Deepak
  • L. K. P. Vignesh
  • G. Srivathsan
  • S. Roshan
  • S. ChandrakalaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)

Abstract

Research over detecting anomalous human behavior in crowded scenes has created much attention due to its direct applicability over a large number of real-world security applications. In this work, we propose a novel statistical feature descriptor to detect violent human activities in real-world surveillance videos. Standard spatiotemporal feature descriptors are used to extract motion cues from videos. Finally, a discriminative SVM classifier is used to classify violent/non-violent scenes present in the videos with the help of feature representation formed out of the proposed statistical descriptor. Efficiency of the proposed approach is tested on crowd violence and hockey fight benchmark datasets.

Keywords

Violence detection Statistical features Histogram of gradients SVM (support vector machines) 

References

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • K. Deepak
    • 1
  • L. K. P. Vignesh
    • 1
  • G. Srivathsan
    • 1
  • S. Roshan
    • 1
  • S. Chandrakala
    • 1
    Email author
  1. 1.Intelligent Systems Group, School of ComputingSASTRA Deemed to be UniversityThanjavurIndia

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