Video background subtraction is a computer vision technique used to separate moving objects (foreground) from the static scene (background) in video feeds, essential for applications like surveillance, motion detection, and object tracking. It involves creating a background model, comparing each new frame to this model, and applying thresholding to identify changes as foreground objects. Methods range from simple frame differencing and running averages to advanced techniques like Gaussian Mixture Models (GMM) and deep learning for handling dynamic scenes. Challenges include dealing with illumination changes, shadows, dynamic backgrounds, and noise. Post-processing is often used to refine results and reduce false positives.