Gaussian Mixture Model Background Subtraction for Motion Detection

This algorithm employs background subtraction methodology using Gaussian Mixture Models (GMM) for motion detection. The implementation typically involves initial background modeling through learning background image statistics over multiple frames. In practical code implementation, each pixel is modeled as a mixture of Gaussian distributions, where the number of Gaussians (usually 3-5) is determined adaptively based on scene complexity. The algorithm compares current frame pixel values against the background model using probability thresholds to identify foreground objects. Key functions include background model initialization, parameter updates using learning rates, and foreground mask generation. After foreground detection, the algorithm enables motion trajectory analysis for object tracking through techniques like Kalman filtering or optical flow, and supports object classification using feature extraction methods. This makes the algorithm widely applicable in video surveillance systems, intelligent transportation solutions, human-computer interaction interfaces, and various computer vision applications.