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If I understand your question correctly, you should implement a sliding window approach. In each window, you should apply the SVM to get candidates. Then, once you've done it for the whole image, you should merge the candidates (if you detected an object, then it is very likely that you'll detect it again in shift of a few pixels - that's the meaning of candidates).

Take a look at the V&J code at openCV or the latentSVM code (detection by parts) to see how it's done there.

By the way, I would use the LatentSVM code (detection by parts) to detect vehicles. It has trained models for cars and for buses.

Good luck.