Privacy protecting visual processing for secure video surveillance

Abstract

Privacy protection is important in video surveillance. In this paper, we address privacy protection related issues. First, based on questionnaire-based experiments we analyze personal sense of privacy from the viewpoint of the relationship between a viewer and a subject. With the analysis results, we introduce a privacy protected video surveillance system named PriSurv, which can adaptively protect subjects' privacy according to their privacy policy against each viewer. Then, we propose two methods of protecting individuals' privacy by controlling the disclosure of subjects' visual information. One uses a set of visual abstraction operators such as silhouette and dot, which gradually control subjects' visual information. The other uses an active appearance model (AAM) based masks which encode privacy information in the original face region. The latter method can be used especially when a subject's expression can be seen in the video and is characterized by the recoverability of the encoded privacy information.