Anonymization in intelligent surveillance systems

Modern surveillance systems collect a massive amount of data. In contrast to conventional systems that store raw sensor material, modern systems take advantage of smart sensors and improvements in image processing. They extract relevant information about the observed objects of interest, which is then stored and processed during the surveillance process. Such high-level information is, e.g., used for situation analysis and can be processed in different surveillance tasks. Modern systems have become powerful, can potentially collect all kind of user information and make it available to any surveillance task. Hence, direct access to the collected high-level data must be prevented. Multiple approaches for anonymization exist, but they do not consider the special requirements of surveillance tasks. This work examines and evaluates existing metrics for anonymization and approaches for anonymization. Even though all kind of data can be collected, position data is still the one with the highest demand. Hence, this work focuses its anonymization and proposes an algorithm that fulfills the requirements for anonymization in surveillance