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A Study on Recognition of Dangerous Behaviors using Privacy Protection Video in Single-person Household Environments

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(5), pp.47-54
  • DOI : 10.9708/jksci.2022.27.05.047
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 21, 2022
  • Accepted : April 21, 2022
  • Published : May 31, 2022

Chae Hyun Lim 1 Myung Ho Kim 1

1숭실대학교

Accredited

ABSTRACT

Recently, with the development of deep learning technology, research on recognizing human behavior is in progress. In this paper, a study was conducted to recognize risky behaviors that may occur in a single-person household environment using deep learning technology. Due to the nature of single-person households, personal privacy protection is necessary. In this paper, we recognize human dangerous behavior in privacy protection video with Gaussian blur filters for privacy protection of individuals. The dangerous behavior recognition method uses the YOLOv5 model to detect and preprocess human object from video, and then uses it as an input value for the behavior recognition model to recognize dangerous behavior. The experiments used ResNet3D, I3D, and SlowFast models, and the experimental results show that the SlowFast model achieved the highest accuracy of 95.7% in privacy-protected video. Through this, it is possible to recognize human dangerous behavior in a single-person household environment while protecting individual privacy.

Citation status

* References for papers published after 2022 are currently being built.