Hello.
Im using Python and OpenCV on my raspberry pi 3 for some kind of object recognition.
I want to do this by applying the HOG + Linear SVM framework for object detection.
My problem is, that i need a dataset for training my detector.I would like to orientate on these five steps(from Pyimagesearch):
1. Extract HOG features from your positive training set.
2. Compute HOG feature vectors from your negative training set.
3. Train your Linear SVM.
4. Apply hard-negative mining.
5. Re-train your Linear SVM using the positive samples, negative samples, and hard-negative samples.
Has someone already made this and could help me? Is there some kind of documentation available? I would appreciate a step by step tutorial, but i already searched and found none.
I hope someone can help me. Kind regards.