Visual context identification for privacy-respecting video analytics

With the growing need for privacy-aware and privacy-respecting CCTV systems, it becomes crucial to develop workflows and architectures that can support and enhance privacy protection. Recent advances in image processing enable the automation of many surveillance tasks, increasing the risks of privacy infringements. Fortunately, image processing and pattern recognition techniques can also be used for automatically evaluating the context in which video surveillance takes place, and can therefore be employed for applying context-specific privacy rules. This paper describes how Bag-of-Visual-Words algorithms as well as human tracking and gait analysis cane used for recognizing specific sub-contexts that necessitate the application of particular privacy protection rules in usage contexts such as ambient assisted living, public or workspace surveillance We explain how the data of a multi-modal surveillance system should be handled in order to avoid unnecessary processing of sensitive information through Image Quality Descriptors that will support visual classifications by computing reliability measures relating to the image quality such as noise or problems with respect to ambient conditions.