Gaussian Mixture Model Based Background Modeling Method

You can reference the Gaussian Mixture Model (GMM) based background modeling method implemented in MATLAB. This approach establishes a background model by decomposing each pixel's color values into multiple Gaussian distributions and employs statistical methods to continuously update the background model, enabling adaptation to environmental changes. The method finds extensive applications in video processing, object tracking, and related fields. By adjusting parameters such as the number of Gaussian distributions, learning rate, and variance thresholds, you can optimize model performance for specific scenarios. In MATLAB implementation, key functions typically include: - Initializing multiple Gaussian distributions per pixel using cv.createBackgroundSubtractorMOG2 or custom implementations - Implementing online Expectation-Maximization (EM) algorithm for parameter updates - Calculating Mahalanobis distance for foreground/background classification - Applying morphological operations for noise reduction in post-processing For enhanced performance, you may explore alternative background modeling approaches such as frame difference methods, texture-based methods (like Local Binary Patterns), or deep learning-based methods (using CNNs) to better suit your specific requirements. The GMM method remains particularly effective for handling gradual lighting changes and periodic motions in dynamic scenes.