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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. 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