Combined CNN/RNN video privacy protection evaluation method for monitoring home scene violence

Abstract

Rapid developments in technology have led to increasing numbers of video surveillance devices in the home environment. The importance of video privacy protection has spurred the development of various video privacy protection methods. This paper proposes a method for evaluating the degree of privacy protection for multilayer compressed sensing video. The combination of CNN convolutional neural network and RNN convolutional network was used to extract video spatial-temporal feature mapping visual privacy protection scores, and the same model was used to map video practicality scores through classifiers. Compared with previous methods, the proposed approach achieves a better prediction effect and generalization performance for videos. Finally, an association model is established between visual privacy protection score and practicability score. This model quantifies the relationship between these aspects, providing suggestions for practical application and enabling the evaluation of other video privacy protection methods.