Preserving and managing privacy information in video surveillance systems

Video surveillance has become a part of our daily lives. Closed-circuit cameras are mounted in countless shopping malls for deterring crimes, at toll booths for assessing tolls, and at traffic intersections for catching speeding drivers. Since the 9-11 terrorist attack, there have been much research efforts directed at applying advanced pattern recognition algorithms to video surveillance. Specifically, searchable surveillance with the help of automatic event detection and human recognition had turned the once labor-intensive monitoring into powerful automated system that can quickly and accurately identify and track visual objects and events. For example, as reported in the Face Recognition Vendor Test (FRVT) in 2006, the best-performing algorithms have already exceeded human capability in face recognition, achieving a false rejection rate of 0.01 at a false acceptance rate of 0.001 [1]. Thus, it is unsurprising that the general public is increasingly wary about the possibility of privacy invasion with video surveillance systems. From the public outcry on the use of face recognition in public events [2] to the report by the American Civil Liberties Union (ACLU) on the surveillance systems’ assault on public’s privacy [3], privacy concerns about surveillance systems can rival those on sensitive financial and medical information. To mitigate these concerns and to facilitate continued development of surveillance technologies, it is imperative to make privacy protection a priority in current and future video surveillance systems.