Video privacy filters with tolerance to segmentation errors for video conferencing and surveillance

It is sometimes desired to obscure background of a person on a video conference or foreground people in a surveillance video. Background subtraction (or foreground detection) methods can help separate desired from undesired planes, however current methods often have errors — holes in foreground or background — especially after lighting changes. We describe a unified approach to video privacy that capitalizes on the realization that private information is often in the image detail, which have edges, rather than in the uniform intensity regions. So a gradient based method for foreground detection can offer both error tolerance and privacy. We show results of error tolerance to lighting change, and degree of privacy gained by foreground and background privacy filters.