Blind background extraction from videos in the cloud

Abstract

Nowadays, more and more people choose to upload their data and information to cloud. The cloud saves users’ data and information and starts providing computing services and artificial intelligence analysis. However, the privacy of users’ information and data will be completely exposed to the cloud without any protection. In this paper, we propose two methods of the blind (privacy-preserving) background extraction from video surveillance for both scenarios with the single-party cloud server (SCS) and multi-party cloud servers (MCS). We combine 1D logistics chaotic encryption with background subtraction based on a mixed Gaussian model, and propose a blind background subtraction method for a single server (SCS) . We combine the Chinese Remainder Theorem (CRT) and the ViBE background subtraction method and propose a multi-server blind background subtraction method (MCS). The test set for the experiment is CDW-2014, the experimental results show that our method has satisfactory results in recognition accuracy, recognition speed, and security analysis. The proposed methods have several advantages: (1) Based on our encryption method, the background extraction method in the original video does not need to be changed; (2) The server does not recognize any valid information for the calculation results; (3) Single cloud server (SCS) uses the chaotic mapping can ensure high-level security and resistance to several attacks; (4) Multiple cloud servers (MCS) can improve data security and improve processing efficiency. This method can accurately extract the background like the original ViBE algorithm while protecting the privacy of client video data.