Computer Vision - Gaussian Pyramid, Scale Space and Pattern Matching
Template matching is related to finding the maximum correlation between two signals, where one of the signals (the image being explored) is usually much bigger than the reference signal (the object being searched for). Obviously, the best correlation score can only be obtained when the size of the object in the image is equal to its size in the template (intuitively, both shapes will superpose).


  However I am not able to relate in any meaningful way the number of pyramid levels to create (for model and for target)


The number of scales is related to two things:


the size of the object of interest in the explored image
the computation time reduction that you want to obtain.


To have the best localization accuracy, you need to have one template image that matches exactly the size of the object in the explored image. Thus, if you don't know a priori this size, but you have instead a range of sizes, your template pyramid should include each of these sizes.

Since the template matching part can be expensive (one correlation computation for each possible position of the template in the image), you want to reduce the number of pixels explored. Thus, you create a pyramid of the image to explore, start by looking for the object at the coarsest scale to get a rough position that gets refined at the next scale, etc. Again, since the size of the object needs to match between the template and the explored image, you need to rescale appropriately each of the images from the pyramid that you built for the template (previous paragraph).


  For example 1 level of the pyramid reduces the resolution of the image by 2 (is this the same as saying that the image has been scaled by 0.5 ) ?


It depends... Reducing the resolution by two along each dimension divides the number of pixels by 4. This is usually referred to as scaling by 0.5 but as an image resolution reduction by 4.

A common practice is to divide the number of pixels by 2, yielding a $\sqrt{2}$ factor along each dimension.


  Than what about a pyramid level of -1 (which should make the image bigger x 2.0) : with what gaussian filter should it be treated before upsampling ?


You need to use image interpolation techniques here. OpenCV uses (in optical flow methods) a gaussian upsampling by injecting $0$'s along the odd lines/columns then applying a gaussian blur.