Efficient Violence Detection in Surveillance.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results-experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.

Authors

  • Romas Vijeikis
    Department of Automation, Faculty of Electrical and Electronic Engineering, Kaunas University of Technology, 51367 Kaunas, Lithuania.
  • Vidas Raudonis
    Department of Automation, Kaunas University of Technology, 51367, Kaunas, Lithuania.
  • Gintaras Dervinis
    Department of Automation, Faculty of Electrical and Electronic Engineering, Kaunas University of Technology, 51367 Kaunas, Lithuania.