Let's start the background subtraction discussion from the beginning. What does a background subtraction process look like? Consider the following image:
The preceding image represents the background scene. Now, let's introduce a new object into this scene:
As shown in the preceding image, there is a new object in the scene. So, if we compute the difference between this image and our background model, you should be able to identify the location of the TV remote:
The overall process looks like this:
There's a reason why we call it the naive approach. It works under ideal conditions, and as we know, nothing is ideal in the real world. It does a reasonably good job of computing the shape of the given object, but it does so under some constraints. One of the main requirements of this approach is that the color and intensity of the object should be sufficiently different from that of the background. Some of the factors that affect these kinds of algorithms...