Gaussian Mixture Model for Background Generation in Video Object Detection

In video object detection, the Gaussian Mixture Model (GMM) serves as an effective approach for background generation, significantly enhancing detection accuracy. As a probabilistic distribution model, GMM decomposes video pixels into a mixture of multiple Gaussian distributions through an iterative expectation-maximization (EM) algorithm. These Gaussian components mathematically represent different moving objects and background elements within the video sequence. The implementation typically involves maintaining 3-5 Gaussian distributions per pixel, with parameters (mean, covariance, and weight) updated recursively using learning rate α (typically 0.001-0.01). Key functions include background model initialization, Mahalanobis distance calculation for pixel classification, and model parameter adaptation. By employing GMM, we achieve superior separation between background and foreground through probabilistic thresholding, thereby improving both the precision and reliability of object detection systems. The model automatically adapts to illumination changes and periodic background motions through its online learning mechanism.