Privacy protecting visual processing for secure video surveillance

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.