Foreground Image Separation via Illumination Compensation
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Solution Overview
Problem
Video surveillance systems face challenges in accurately separating foreground and background images due to variations caused by automatic white balance and exposure control functions, leading to poor performance under different illumination conditions.
Innovation Solution
A method is introduced that involves continuous white balance and brightness compensation using a reference image to stabilize background color and brightness, allowing for accurate foreground image separation through a Gaussian Mixture Model-based background subtraction algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If automatic white balance or exposure control function is activated to improve image quality under different illumination conditions, then image quality is improved, but background color and brightness change, leading to poor foreground image separation performance
Solution Approach 1:
The patent applies preliminary action by performing white balance and brightness compensation before foreground image separation. The system estimates adjustment parameters from the input image and applies them to compensate for automatic adjustment effects, stabilizing background characteristics before the separation process occurs.
Solution Approach 2:
The patent implements feedback by using the input image itself to estimate the automatic adjustment parameters. The system analyzes the input image to determine what adjustments were made, then uses this information to compensate for those adjustments, creating a closed-loop control that maintains background stability.
2Reliability
If image texture features are used to overcome automatic white balance issues, then foreground separation is improved under certain conditions, but performance becomes poor under specific conditions due to complementary properties between texture and color parameters
Solution Approach 1:
The patent merges multiple approaches by combining white balance/brightness compensation with background subtraction algorithms. Instead of relying solely on texture features or color parameters, the system integrates parameter estimation and compensation with traditional background subtraction methods, creating a more robust solution that works across different conditions.
Data Source
AI summary
A foreground image separation method is disclosed to separate dynamic foreground and static background in a sequence of input images which have been processed either with automatic white balance or brightness control by the camera. First, an input image is received from the camera, and then a white balance or brightness compensation is performed to the input image according to a reference image to generate a compensated image with background color and background brightness which are approximately similar to that of the reference image. Finally, a background subtraction algorithm is performed to the compensated image to generate a background separation result. The background subtraction algorithm could be a Gaussian Mixture Model based algorithm. The method could process successive images received from the camera to continuously generate background separation results and update the reference image accordingly, such that video surveillance system could adapt to the change of illumination.