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If you have a large enough number of court and non-court examples, maybe you can train a HAAR or LBP cascade classifier (similar to the ones used to detect faces) to do the job. The advantage against Mahdi's proposal (although his proposal is as valid as any other) is that you don't need to deal with color extraction (not all courts are green and the ones that are do not have the same tone of green, and all that) as you input the images in grayscale for feature detection. Also no explicit line detection is needed.

If the courts have always the same proportions in your images, what you can do to avoid having to train a different model for each orientation is to train a model in a standard orientation (longitudinal orientation for instance) and at test time rotate the same input image to different orientations and perform the detection against the standard-oriented court model.

I agree with Mahdi, this is no pet project AT ALL you'll see! hehehehe

Good luck!