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Ok lets take the time to answer some of your questions. As mentioned before by @GilLevi, always try out the available models first before going into training your own detector model. It will take time and parameter tuning to reach an effective model.

1) How much time does it take to train a classifier using LBP for detecting cars?

I trained an LBP model in a couple of hours, resulting in a 20 stage classifier with poorer performance. In order to increase the performance you can use hard negative mining and retraining using those negatives to reach better results.

However it all depends on the situation where you want to detect cars. Is it aerial like in the topic mentioned, then you need to train your own model. Is it sideways? Then you can use prebuilt models that are already available in openCV.

2) What was the ratio of positive and negative images?

As far as I understand the training of object models, it depends on the variance in the data. If you cannot isolate the important training data that will form the decision border of your classifier, then a ratio of 1.000 positives and 10.000 negatives will get you quite far. If you however use just a plain set without prior knowledge, getting a decent classifier could lead you to data amounts of 10.000 positives and 100.000 negatives.

3) Which dataset did you use to train your LBP classifier? 6) Is it possible to share the classifier?

A private dataset of a company I am working together with. Due to this and the NDA I have signed I cannot share any data nor the trained model.

4) How long does it take to train the classifier?

It took me about 2-3 days to train a decent basic classifier adjusting parameters and performing hard negative mining.

5) Were you able to make a comparison of HaarCascade Classifier and LBP?

Yes. I always start training using LBP. If your context of the application is less complex, several hunderd of training images can result in a very nice detector in only a couple of hours. Compared to that the HAAR training takes up to a week and longer for the same result. It depends on many things, including the current implementation which is far from optimal.