Privacy-Preserving Cloud-Based Video Surveillance with Adjustable Granularity of Privacy Protection
Xiaojing Ma, Huan Peng, Hai Jin, Bin B. Zhu · 2018
Cloud-based video surveillance requires protecting privacy yet allowing cloud to perform motion detection and tracking. Prior art allows performing surveillance on encrypted videos but details of trajectories of moving objects are exposed. In this paper, we propose a video surveillance system that supports adjustable granularity of motion detection and tracking for fine-grained control on conflicting requirements between privacy protection and accuracy of motion detection and tracking. Our encryption adds a permutation layer to conventional format-compliant selective encryption to ensure motion information can be recovered only at a given granularity yet incurs a very small impact on the bitrate and processing speed of video compression. Our motion detection method estimates both local and global motions to distinguish foreground motions from background motions and deduce trajectories of foreground moving objects. Experimental results indicate that our system has fulfilled the design goal.