Detection of objects from images or from video is no trivial task. We need to use some type of machine learning algorithm and train it to detect features and also identify misses and false positives. The haartraining algorithm does just this. It creates a series of haarclassifiers which ensure that non-features are quickly rejected as the object is identified.
This post will highlight the necessary steps required to build a haarclassifier for detection a hand or any object of interest. This post is sequel to my earlier post (OpenCV: Haartraining and all that jazz!) and has a lot more detail. In order to train the haarclassifier, it is suggested, that at least 1000 positive samples (images with the object of interest- hand in this case) and 2000 negative samples (any other image) is required.
As before for performing haartraining the following 3 steps have to be performed
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