OpenCV positive samples dimensions?

Question

So I've come across lots of tutorials about OpenCV's haartraining and cascaded training tools. In particular I'm interested in training a car classifier using the createsamples tool but there seem to be conflicting statements all over the place regarding the -w and -h parameters, so I'm confused. I'm referring to the command:

$ createsamples -info samples.dat -vec samples.vec -w 20 -h 20

I have the following three questions:

• I understand that the aspect ratio of the positive samples should be the same as the aspect ratio you get from the -w and -h parameters above. But do the -w and -h parameters of ALL of the positive samples have to be the same size, as well? Eg. I have close to 1000 images. Do all of them have to be the same size after cropping?

• If it is not the size but the aspect ratio that matters, then how precisely matching must the aspect ratio be of the positive samples, compared to the -w and -h parameters mentioned in the OpenCV tools? I mean, is the classifier very sensitive, so that even a few pixels off here and there would affect its performance? Or would you say that it's safe to work with images as long as they're all approximately the same ratio by eye.

• I have already cropped several images to the same size. But in trying to make them all the same size, some of them have a bit more background included in the bounding boxes than others, and some have slightly different margins. (For example, see the two images below. The bigger car takes up more of the image, but there's a wider margin around the smaller car). I'm just wondering if having a collection of images like this is fine, or if it will lower the accuracy of the classifier and that I should therefore ensure tighter bounding boxes around all objects of interest (in this case, cars)?

Alvy

I can only comment that there are much better approaches for cars detection than haar/LBP cascades. – Bichromate

Well I can't help this because I've been hired to follow this approach, but what are better approaches in your opinion, anyway? – Alvy

latent-svm-detector. You can get it running in no time and it would give far better results. – Bichromate

thanks... could it be extended to classify cars from different viewing angles? – Alvy

It can detect cars from various viewpoints. It gave me very nice results when I tested it on cars. – Bichromate

@GilLevi, Have you been able to train the latent-svm-detector on your own data? – Alvy

I was able to train the latent-svm-detector on my own data using the Matlab package, but couldn't convert the model to the OpenCV format. – Bichromate

So did you manage to successfully test it on your data? (If not using OpenCV, then using the matlab detection code). – Alvy

I did, way back then. But I think there are much stronger methods nowadays – Bichromate

Answer 1

First Question: Yes, all the images to be used for training have to be the same size. (at least for the last time I did face detection sample training. Should be the same here. If I am not wrong, there will be an error if the images are not of same size. But u can try it out and see if time permits.)

Second Question: Not really sure what you are asking here. But the classifier is not that sensitive as u think. A few pixels off the object of interest, let's say the hand for instance, if the little finger is missing a few pixels(due to cropping) and other images have few pixels missing for the thumb, etc... the classifier will still be able to detect the hand. So a few pixels missing here and there or a few background pixels added in, will not affect the classifier much at the end of the day.

Third Question: You should crop the image to consist of the car only for maximum result. try eliminate as much background as possible. I did a research based on samples with noisy background, black background and cropped samples with minimum background. Cropped samples with minimum background shows the best results in terms of false positive and false negative, from what I remember.

U can use object marker to do it: http://achuwilson.wordpress.com/2011/02/13/object-detection-using-opencv-using-haartraining/

The tedious way would be to use paint to resize all the image to the same pixel value after cropping.

This link should also answer your question: http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html

I also agree with GilLevi that there are much better detection methods compared to Haar, HoG, LBP cascade. training of the images can take days(depends on number of images trained). If you really have to use the cascade methods and you are looking to minimise training time, training with Haar-like features takes much longer than with HoG or LBP. But results wise, I am not really sure which will ensure better performance and robustness.

Hope my answer helped you. Should there be more questions, do comment.

Broucek

thanks... I have another question here: #21402327, any idea? – Alvy

Sure. I take a look... The answers here did help u right? Its based on my experience in classifiers training of approximately 2 years. – Broucek

oh yes, definitely helped! – Alvy