Video Background Subtraction

Video background subtraction is a technique in the field of computer vision aimed at separating moving objects (foreground) from static scenes (background) in video streams. This technology works by constructing a background model, comparing each frame with the model, and applying thresholding to identify changed regions as foreground objects. Video background subtraction is of significant value for applications such as surveillance, motion detection, and object tracking, effectively addressing challenges like lighting changes, shadows, dynamic backgrounds, and noise. Common methods include frame differencing, running average, Gaussian Mixture Model (GMM), and deep learning. Post-processing techniques are often used to optimize the results and reduce false alarms.