Motion Object Detection Based on Gaussian Mixture Model

In this document, I present a motion object detection method based on Gaussian Mixture Model (GMM) implemented using MATLAB. This approach effectively identifies moving objects in video sequences and marks them with annotations. By employing Gaussian Mixture Models, we can model each pixel in the video stream, enabling accurate detection of moving objects' positions and trajectories. The implementation typically involves key MATLAB functions like vision.ForegroundDetector with GMM parameters for adaptive background modeling, where each pixel is represented by a mixture of Gaussian distributions that get updated frame-by-frame. The algorithm calculates the probability of pixels belonging to background/foreground models, with foreground objects detected when pixel values deviate significantly from learned background distributions. This method proves highly valuable in numerous applications such as video surveillance, autonomous driving systems, and traffic monitoring. The implementation may include morphological operations like imopen and imclose for noise reduction, and bounding box creation using regionprops function for object marking. Both in academic research and engineering practices, this motion detection approach demonstrates extensive application prospects with its robustness to lighting changes and multimodal background scenarios.