Gaussian Mixture Model for Background Modeling

In this text, we employed the Gaussian Mixture Model (GMM) method to establish motion-background modeling in dynamic videos and achieved exceptional results. Gaussian Mixture Model background modeling is a widely-used technique that models each pixel in the video sequence using multiple Gaussian distributions, enabling more accurate detection and separation of stationary background and moving objects. The implementation typically involves maintaining a mixture of K Gaussian distributions per pixel (commonly K=3-5), where each distribution represents different background/foreground states. Key algorithmic steps include: background model initialization using initial frames, pixel classification based on Mahalanobis distance, online parameter updating using learning rates, and background model maintenance through weight sorting and replacement strategies. By utilizing this method, we obtained more accurate and reliable background modeling results in complex video scenarios, thereby enhancing the effectiveness of subsequent video analysis and processing tasks. Our research demonstrates that Gaussian Mixture Model background modeling is an efficient and robust approach suitable for various types of video application scenarios, including surveillance systems, traffic monitoring, and human-computer interaction systems.