Learning OpenCV, 2nd Edition

public:

virtual ~BackgroundSubtractor();

virtual void apply(

cv::InputArray image,

cv::OutputArray fgmask,

double learningRate = 0

);

virtual void getBackgroundImage(

cv::OutputArray backgroundImage

) const;

};

As you can see, after the destructor, there are only two methods definedF

16

F. The first is the apply()

function, which in this context is used to both ingest a new image and to produce the calculated foreground

mask for that image. The second function produces an image representation of the background. This image

is primarily for visualization and debugging; after all there is much more information associated with any

single pixel in the background than just a color. As a result the image produced by

getBackgroundImage() can only be a partial presentation of the information that exists in the

background model.

One thing that might seem to be a glaring omission is the absence of a method that accumulates

background images for training. The reason for this is that there came to be (relative) consensus in the

academic literature that any background subtraction algorithm that was not essentially continuously

training was an undesirable algorithm. The reasons for this are many, with the most obvious of which being

the effect of gradual illumination change on a scene (e.g., as the sun rises and sets outside the window). The

more subtle issues arise from the fact that ...