Adaptive cartooning for privacy protection in camera networks
Ádám Erdélyi, Tibor Barat, Patrick Valet, Thomas Winkler, Bernhard Rinner · 2014
Visual privacy in video-based applications such as surveillance, assisted living or home monitoring is a highly active research topic. It is critical to protect the privacy of monitored people without severely limiting the utility of the system. We present a resource-aware cartooning privacy protection filter which converts raw images into abstracted frames where the privacy revealing details are re-moved. Cartooning can be applied either to entire images or pre-selected sensitive regions of interest. We provide an adaptation mechanism to our cartooning technique where the operator can easily change the filter intensity. The feasibility of this new approach is demonstrated by its deployment to real-world embedded smart cameras. We evaluate privacy protection and utility of cartooning with the PEViD data set and compare it with the two widely-used privacy filters: blurring and pixelation.