Learning OpenCV, 2nd Edition

In the case of our background model, we will learn a codebook of boxes that cover three dimensions: the

three channels that make up our image at each pixel.

X

Figure 9-5

X visualizes the (intensity dimension of the) codebooks for six different pixels learned from the data

in

XFigure 9-1X.F

10

F This codebook method can deal with pixels that change levels dramatically (e.g., pixels in a

windblown tree, which might alternately be one of many colors of leaves, or the blue sky beyond that tree).

With this more precise method of modeling, we can detect a foreground object that has values between the

pixel values. Compare this with

XFigure 9-2X, where the averaging method cannot distinguish the hand value

(shown as a dotted line) from the pixel fluctuations. Peeking ahead to the next section, we see the better

performance of the codebook method versus the averaging method shown later in

X

Figure 9-8X.

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In this case, we have chosen several pixels at random from the scan line to avoid excessive clutter. Of course, there is

actually a codebook for every pixel.

Figure 9-5: Intensity portion of learned codebook entries for fluctuations of six chosen pixels (shown as

vertical boxes): codebook boxes accommodate pixels that take on multiple discrete values and so can better

model discontinuous ...