Haartraining vs Traincascade : Object Detection

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@Maria: another guy that didn't know of traincascade :D

[Bia6969] So, let's say i wanna change my root and try traincascade + LBP, correct me if i'm wrong:

-I'd still have positive and negative folders. I'd still create a vector through the opencv_createsamples. - The difference would be at the trainning stage, right? I'd use opencv_TRAINCASCADE INSTEAD OF opencv_HAARTRAINING.

-Then what? I understand i'd have to change my detect.cpp, but how? There aren't that many code examples from traincascade. i mean, my detect uses cvHaarClassifierCascade and i'm guessing i'd have to use other structs, but where can i see which ones?

[Maria] The usage of traincascade is very similar to the usage of haartraining app. Some parameters have slightly different names. There are some new ones e.g. "-featureType" (HAAR or LBP). See the doc on training http://docs.opencv.org/doc/user_guide/ug_traincascade.html. For detection there is a class CascadeClassifier. See the doc on detection http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html#cascadeclassifier. It can work with old and new file format of trained cascade. Example of its using is facedetect.cpp sample.

Next time please ask a clarifying question as a comment for the order. It's really not an answer. Or, if it's independent, open a new question thread (this time it's not this case). Thanks.

Is there any pretrained LBP cascades for faces\face parts? How training time depends on number of samples?

Yes, a couple of trained LBP cascades can be found here http://code.opencv.org/projects/opencv/repository/revisions/master/show/data/lbpcascades. I don't know how lbpcascade_profileface.xml was trained (e.g. which vec-file was used). lbpcascade_frontalface.xml was trained by me on vec-file http://code.opencv.org/projects/opencv/repository/revisions/master/changes/data/vec_files/trainingfaces_24-24.vec; the used parameters of traincascade app are listed in the cascade xml. At that time I did not have a goal to train as good as possible LBP cascade, so that cascade is just an example (but working well !) and I'm sure you can train even better LBP cascade for faces.

Of course, more numPos and numSet you set, more time is need. It's hard to say exactly how training time depends on a number of samples because it's also highly depends on your training datasets and other parameters. From my experience the most time-consuming part of traincascade is selecting the negative examples to train each new stage because they have to be recognized as positive samples by all previous (already trained) stages. I.e. traincascade spends significant time in searching the samples of negative base that are very similar to positives (faces).

even in other forums it says LBP is the most convenient to use since it has fastest training. But i am curious if cascade training can detect object in different orientation and rotation? can it be compensated with rotated positive samples?

@bertumen.wj, please don't post answers, if you have a question or comment.

Also, let me please ask, if you have a question in 2016, about something in 2012 then it is always better to open a new question and link the old one inside it. The users of this questions are almost all disappeared from the scene, apart from some core devs. Furthermore your question will get lost here.

So please, open up a new question and stop opening arcane old ones!

Can I use features other than haar, lbp or hog? If so what are the modifications I need to make?