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Hi, I also asked my self those questions I can tell you from my experience.(By the way look in this forum for more answers in this topic there are many of those!)

Regarding your's question. 1.Your positive and Negative images must be correlated to the problem scenario you would like to solve!(Is there a typical background lighting condition ...) Try to have positive and negative samples that span the problem that you are trying to solve. 2.The positive samples should be approximately in the same rotation \view point in all Images. If you need several viewpoints train several classifiers one for each viewpoint. 3.Use train_cascade instead of haartraining and use LBP feature for faster training. 4.Start with default params. 5.Try to use for example npos = 5-- and Nneg =2000 for start and check your result if it's not good enough add later more positive and negative samples.

Also as I said look here in this forum question on this topic.

Good luck!

Hi, I also asked my self those questions I can tell you from my experience.(By the way look in this forum for more answers in this topic there are many of those!)

Regarding your's question. your question:

1.Your positive and Negative images must be correlated to the problem scenario you would like to solve!(Is there a typical background lighting condition ...) Try to have positive and negative samples that span the problem that you are trying to solve. solve.

2.The positive samples should be approximately in the same rotation \view point rotation\view-point in all positive Images. If you need several viewpoints train several classifiers one for each viewpoint. viewpoint.

3.Use train_cascade instead of haartraining and use LBP feature for faster training. training.

4.Start with default params. params.

5.Try to use for example npos = 5-- and Nneg =2000 for start and check your result if it's not good enough add later more positive and negative samples.

Also as I said look here in this forum question on this topic.

Good luck!