Simulation of Moving Object Detection Algorithm Based on Gaussian Mixture Model

The Gaussian Mixture Model (GMM) is a classical algorithm widely used for moving object detection in video sequences. This algorithm models each pixel in video frames using multiple Gaussian distributions, enabling adaptation to complex and dynamic background environments.

In moving object detection, the core concept of GMM involves representing the background as a mixture of multiple Gaussian distributions. The intensity variations of each pixel at different time points are modeled as weighted combinations of these distributions. By continuously updating Gaussian parameters (mean, variance, and weight), the algorithm dynamically differentiates between background and foreground pixels.

The simulation implementation typically follows these steps: First, initialize mixture model parameters for each pixel. Then, for each video frame, update the corresponding Gaussian distributions based on current pixel values. Next, sort the distributions by weight and variance to select the most probable background distributions. Finally, determine whether a pixel belongs to a moving object by comparing its current value with the background model.

Key implementation aspects include: Using OpenCV's BackgroundSubtractorMOG2 class for efficient GMM implementation, where developers can configure parameters like history length and mixture components. The algorithm maintains a probability distribution for each pixel, with typical implementations using 3-5 Gaussian components per pixel. The update process involves matching current pixel values to existing distributions and adapting parameters using learning rate controls.

GMM's advantages include effective handling of illumination changes and minor background movements. However, its computational complexity requires optimization for real-time applications. Parameter selection (number of distributions, learning rates) significantly impacts detection performance and requires experimental tuning. In computer vision, GMM-based detection serves as a preprocessing step for video surveillance and intelligent transportation systems, providing foundation for subsequent object tracking and behavior analysis.