I've found Waldo!
How I've done it
First, I'm filtering out all colours that aren't red
waldo = Import["http://www.findwaldo.com/fankit/graphics/IntlManOfLiterature/Scenes/DepartmentStore.jpg"];
red = Fold[ImageSubtract, #[[1]], Rest[#]] &@ColorSeparate[waldo];
Next, I'm calculating the correlation of this image with a simple black and white pattern to find the red and white transitions in the shirt.
corr = ImageCorrelate[red,
Image@Join[ConstantArray[1, {2, 4}], ConstantArray[0, {2, 4}]],
NormalizedSquaredEuclideanDistance];
I use Binarize to pick out the pixels in the image with a sufficiently high correlation and draw white circle around them to emphasize them using Dilation
pos = Dilation[ColorNegate[Binarize[corr, .12]], DiskMatrix[30]];
I had to play around a little with the level. If the level is too high, too many false positives are picked out.
Finally I'm combining this result with the original image to get the result above
found = ImageMultiply[waldo, ImageAdd[ColorConvert[pos, "GrayLevel"], .5]]
An alternative approach would be to extract features (keypoints) using the scale-invariant feature transform (SIFT) or Speeded Up Robust Features (SURF).
You can find a nice OpenCV code example in Java, C++, and Python on this page: Features2D + Homography to find a known object
Both algorithms are invariant to scaling and rotation. Since they work with features, you can also handle occlusion (as long as enough keypoints are visible).
Image source: tutorial example
The processing takes a few hundred ms for SIFT, SURF is bit faster, but it not suitable for real-time applications. ORB uses FAST which is weaker regarding rotation invariance.
The original papers
Best Answer
These posts will get you started:
How to detect circles
How to detect squares
How to detect a sheet of paper (advanced square detection)
You will probably have to adjust some parameters in these codes to match your circles/squares, but the core of the technique is shown on these examples.