Single Gaussian Background Modeling with Background Subtraction Method

In image processing, the Single Gaussian Model serves as a widely-used method for background extraction, particularly effective in scenarios with stable and uniform backgrounds. This model stands out for its simplicity and computational efficiency, as it avoids repetitive modeling by adopting an iterative parameter update approach, where t denotes the time index. For each pixel, the current color intensity xt is evaluated against a predefined probability threshold. If xt is less than or equal to this threshold, the pixel is classified as foreground; otherwise, it is identified as background. From an implementation perspective, the algorithm typically initializes a Gaussian distribution for each pixel using initial mean and variance values. During processing, these parameters are updated iteratively using a learning rate (e.g., μ_t = (1-α)μ_{t-1} + αx_t), enabling gradual adaptation to slow background changes. Key functions in code would involve calculating the Mahalanobis distance between xt and the model to determine the threshold comparison, followed by segmentation into foreground/background masks. Thus, the Single Gaussian Model facilitates effective differentiation between foreground and background elements, supporting advanced image analysis and processing tasks such as motion detection or object tracking.