Background Blurring System for Video Compression Stream
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Solution Overview
Problem
Existing video compression streams from video cameras, used for surveillance, incur high computational costs and bitrate requirements, with background blurring techniques partially addressing privacy concerns but not efficiently reducing bitrate and computational load.
Innovation Solution
A background blurring system that identifies background pixels in a video stream, applies blur values, and updates pixel values based on changes in pixel position between images, reusing blurred values within a predetermined time period to minimize computational effort and bitrate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If background blurring is applied to video streams, then privacy protection is improved and bitrate consumption is reduced, but computational cost increases and implementation complexity arises
Solution Approach 1:
The system performs preliminary background analysis by comparing the current frame with a reference frame to identify background pixels before applying blurring. This preliminary identification of background regions (Step S102) allows the system to prepare blur masks in advance, reducing real-time computational burden while maintaining privacy protection effectiveness.
Solution Approach 2:
The video processing is segmented into distinct stages: background identification through frame comparison (Step S102), blur mask generation (Step S104), and selective application of blurring (Step S106). This segmentation allows each component to be optimized independently, managing implementation complexity while achieving privacy protection.
2Loss of information
If background blurring is applied continuously, then privacy protection is maintained, but bitrate reduction is limited and computational cost increases
Solution Approach 1:
Instead of applying blurring to the entire video stream continuously, the system applies blurring selectively only to identified background regions (Step S106). This partial action approach maintains privacy protection for background areas while preserving foreground quality, achieving better bitrate reduction efficiency without sacrificing privacy.
Solution Approach 2:
The system periodically updates background identification by comparing frames at intervals rather than processing every single frame. The reference frame is updated periodically (Step S102), and blur masks are regenerated only when necessary, reducing computational cost while maintaining continuous privacy protection through selective re-application of blurring.
3Measurement precision
If pixel values are updated continuously in video stream, then image quality is maintained, but bitrate consumption increases
Solution Approach 1:
The system discards redundant pixel information by identifying static background regions and replacing their pixel values with blurred versions (Step S106). The original high-precision background pixel data is discarded in favor of compressed blur representations, significantly reducing bitrate consumption while maintaining image quality for dynamic foreground regions that retain their original pixel values.
Solution Approach 2:
For background regions, instead of transmitting full-resolution pixel data continuously, the system creates blurred copies of background areas (Step S106). These blurred copies serve as sufficient representations for privacy protection purposes, reducing the quantity of data transmitted while maintaining acceptable image quality for surveillance purposes.
Data Source
AI summary
The present disclosure relates to a method performed by a background blurring system (1) for provision of a video compression stream from a video camera (2) adapted for capturing a scene. The background blurring system identifies (1001) in a first image of the scene, at a first point in time, at least a first pixel comprised in a background of the first image. The background blurring system further determines (1002) a blurred pixel value for the at least first pixel. Moreover, the background blurring system provides (1003), subsequent the first point in time, the blurred pixel value in the video compression stream. The background blurring system furthermore identifies (1004) in a second image of the scene, at a subsequent second point in time, that the at least first pixel has altered to be comprised in a foreground of the second image. Moreover, the background blurring system provides (1005) in the video compression stream, subsequent the second point in time—continuously and/or intermittently—identified non-blurred pixel values for the at least first pixel. The background blurring system further identifies (1006) in a third image of the scene, at a subsequent third point in time, that the at least first pixel has altered to be comprised in a background of the third image, wherein a value of the at least first pixel has remained unchanged from an intermediate point in time up to the third point in time. Furthermore, when the third point in time is within a predeterminable background merge time period from the intermediate point in time, the background blurring system provides (1007), subsequent the third point in time, the blurred pixel value in the video compression stream.The disclosure also relates to a background blurring system in accordance with the foregoing, a video camera comprising such a background blurring system, and a respective corresponding computer program product and non-volatile computer readable storage medium.