Due to some tragic events such as crime, bank robberies and terrorist attacks, an unparalleled surge in video surveillance cameras has occurred in recent years. In consequence, our daily life is overseen everywhere. At the same time, automatic processing technology and quality of sensors have advanced significantly, which has even enabled automatic detection, tracking and identification of individuals. With the proliferation of video surveillance systems and the progress in automatic recognition, privacy protection is now becoming a significant concern.
While privacy is essential to freedom, surveillance is a major actor for our safety. The purpose of this thesis is to design technological solutions to the issue of privacy protection of individuals while preserving the utility of the surveillance (i.e. preserving the understanding of the scene and enabling the re-identification of a person in case of trouble). Indeed, an ideal surveillance system should protect the personal privacy of individuals while still providing a high level of the utility of visual surveillance.
Existing methods have issues to manage this trade off, usually, the increase in utility of surveillance brings about a significant decrease in personal privacy.
To address privacy concerns regarding digital image or video surveillance cameras, we propose one main concept: using the most visible information of the image to preserve the ability to recognize actions while protecting identities by encrypting and hiding the original information in the least visible information of the image. This fulfils a better trade off between privacy and safety of people compared to the existing methods in that domain.