In the Shi-Tomasi method, is it possible to get the area where the frequency of detected corners is high?

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Depending on what you want to do, you can estimate the density of your corners as follows:

1. Make a Density Estimator image

   1. Make a 64x64 black image
   2. Draw a 16x16 radius white circle at its center
   3. Gaussian Blur it with a filter size of ~15
   4. You now have a 2D "gaussian" pattern you can use as a density estimator

2. Create a black image of the same size as your input (say 640x480)

3. For each of your detected corners
   1. Add the Density Estimator image to the black image with a small weight (you'll be adding a lot of them)
4. You now have an image that has blobs that become very white if there are a lot of edges in that region, and remains black where you dont have them

You can then use that image to find regions of high density. The only parameter you have to play with is the size of your Estimator image (larger circle == smoother output).

Incidentally, this is exactly what a RBF kernel does.

However

if you only want to know the "center of mass" of your corners (i.e. you only want ONE answer, and not a density) you can do it much more easily, and FooBar's solution is also perfect for that (and much faster).

You should have a look at algorithms like meanshift or camshift.