Understanding background subtraction
Background subtraction is very useful in video surveillance. Basically, the background subtraction technique performs really well in cases where we have to detect moving objects in a static scene. How is this useful for video surveillance? The process of video surveillance involves dealing with constant data flow. The data stream keeps coming in and we need to analyze it to recognize any suspicious activity. Let's consider the example of a hotel lobby. All the walls and furniture have a fixed location. If we build a background model, we can use it to identify suspicious activity in the lobby. We are taking advantage of the fact that the background scene remains static (which happens to be true in this case). This helps us avoid any unnecessary computational overhead. As the name indicates, this algorithm works by detecting and assigning each pixel of an image to two classes, either the background (assumed static and stable) or the foreground, and subtracting...
