Abstract

Background blurring is an effective way to both preserving privacy and keeping communication effective during video conferencing. This paper proposes a light weight real-time algorithm to perform background blurring using a fast background modeling algorithm combined with a face detector/tracker. A soft decision is made at each pixel whether it belongs to the foreground or the background based on multiple vision features. The classification results are mapped to a per-pixel blurring radius image to blur the background. The algorithm produces satisfactory results under a wide range of conditions, and occupies less than 30% of the CPU cycles on a 3GHz Pentium 4 machine without further optimization.