How does HOG feature descriptor training work?

There doesn't seem to be any implementations of HOG training in openCV a开发者_开发问答nd little sources about how HOG training works. From what I gathered, HOG training can be done in real time. But what are the requirements of training? How does the training process actually work?

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As with most computer vision algorithms, Google Scholar is your friend :) I would suggest reading a few papers on how it works. Here is one of the most referenced papers on HoG for you to start with.

Another tip when researching in computer vision is to note the authors of the papers you find interesting, and try to find their websites. They will tend to have an implementation of their algorithms as well as rules of thumb on how to use them. Also, look up the references that are sited in the paper about your algorithm. This can be very helpful in aquiring the background knowledge to truly understand how the algorithm works and why.

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Your terminology is a bit mixed up. HOG is a feature descriptor. You can train a classifier using HOG, which can in turn be used for object detection. OpenCV includes a people detector that uses HOG features and an SVM classifier. It also includes CascadeClassifier, which can use HOG, and which is typically used for face detection.

There is a program in OpenCV called opencv_traincascade, which lets you train a cascade object detector, an which gives you the option to use HOG. There is a function in the Computer Vision System Toolbox for MATLAB called trainCascadeObjectDetector, which does the same thing.