Custom HOGDetector using CvSVM and HOG features.
I'm trying to train my own detector for use with OpenCV::HOGDescriptor but I'm having trouble making the existing HOGDescriptor work with my newly trained SVM.
I have calculated HOG features for positive and negative training images, labeled them and trained the SVM using CvSVM. The parameters I have used are:
CvSVMParams params;
params.svm_type =CvSVM::EPS_SVR;
params.kernel_type = CvSVM::LINEAR;
params.C = 0.01;
params.p = 0.5;
Then I calculate Primal Form of the support vectors so that I only get one vector instead of many and set the calculated support vector using HOGDescriptor.setSVMDetector(vector);
When I use CvSVM.predict() I am able to correctly classify objects with the SVM with high accuracy, but HOGDescriptor.detect() or detectMultiScale() always returns a lot of positive matches and does not give accurate predictions.
Here is my code for flattening the vector.
I am not able to find much information about how to make the trained svm model work with HOGDescriptor. CvSVM.predict() uses the original trained support vectors for classification so there might be something wrong with the way I'm calculating primal form to feed into HOGDescriptor.setSVMDetector(). Have anyone tried something similar and can point me in the right direction?
