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ok, let's summarize the 2 approaches.

if you want to detect a single object class (detectMultiscale):

• train:
• crop all positive and negative images to same windowsize(e.g. 24x24). this is the minimum size, that can be detected later
• use train_HOG.cpp (from the samples), to train an SVM(regression), and save it
• detect:
• load the single, pretrained SVM support vector into the HOGDescriptor
• detectMultiscale on an (abitrary sized) grayscale image.

if you want to classify multiple traffic signs:

• train:
• crop all images to same windowsize(e.g. 24x24) (same as above)
• get HOG descriptors for each, reshape them to a single row, and push_back all of them into a single large Mat. you also need a "labels" Mat, containing the class id for each descriptor

• train an SVM (or ANN, or KNN) with this data and labels (classification)

• test:

• find contourscontours in large image
• get boundingRect() of it
• get image ROI (crop) of that rect, resize to the HOG windowsize, you trained your SVM on
• get HOG feature from that ROI
• predict()