Human Motion Detection Using Single Gaussian Model

In the MATLAB environment, single Gaussian modeling can be employed for human motion detection. This approach accurately identifies human motion contours in images and further optimizes detection results through mathematical morphology processing. The implementation typically involves creating a background model using the im2double and rgb2gray functions for image preprocessing, followed by Gaussian mixture model fitting with normpdf for probability density estimation. Morphological operations like imopen and imclose can refine detected contours by removing noise and filling gaps. For enhanced accuracy, detected human contours can be matched against predefined shape templates using correlation methods or contour matching algorithms. Additional image processing techniques such as convolutional neural networks (CNN) can be integrated for classification tasks, improving the recognition of human poses and actions through deep learning frameworks like Deep Learning Toolbox. The single Gaussian model proves particularly effective for high-precision detection scenarios, offering reliable performance when combined with appropriate post-processing techniques.