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

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.

Automated summary

Rapid technological advancements have led to an increase in the number of video surveillance devices in homes. This has prompted the development of various methods for video privacy protection. This paper proposes a method for evaluating the level of privacy protection in multilayer compressed sensing videos. By using a combination of CNN and RNN convolutional networks, the proposed approach achieves better prediction and generalization performance compared to previous methods. Additionally, an association model is established between visual privacy protection score and practicability score, allowing for practical applications and evaluation of other video privacy protection methods.