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Ok I guess I can have a go at trying to answer your problem right now.
As far as I see you are mixing up two things. The detect and detectMultiScale are two algorithms that are used with the xml cascade classifiers created by the traincascade algorithm.
However I know they can also be used for classification with a SVM model, since it is done in the latentSVM example, based on the Felzenszwalb detector. However, since I did not manually train a SVM model yet (not there yet in my research - still doing cascade classifiers first) I would suggest checking the following stuff:
1) Did you cross referenced your model with the existing pedestrian model in the resource folder of OpenCV? Do you have the same structure? The same parameters? The same elements?
2) A problem with using SVM detector is that you define support vectors based on a maximal margin approach using slack variables to determine the allowed mistakes. However, you always do it based on the provided data an the estimated data distribution from this. Can you point out how many samples you used? It is possible that you did not sample background from object enough, being negatives and positives.
Get back to me with the numbers please.
