# Training of SVM classifier in OpenCV using HOG, SIFT and ORB features

I'm trying to train a SVM classifier to recognize pedestrians in a set of 64x128 images. I've already done that using HOG features, and now I need to implement the same thing using SIFT and ORB. Now I have a problem: I can not extract the SIFT features from images, nay, I extract the features but I found out that most of desriptors have a 0 value.

This is a piece of the code:

DenseFeatureDetector detector;
SiftDescriptorExtractor descriptor;
vector<KeyPoint> keypoints;

//for every image I compute te SIFT
detector.detect(image, keypoints);
Mat desc;
descriptor.compute(image,keypoints, desc);
Mat v(1,30976,CV_32FC1);
for (int j = 0; j<desc.rows; j++){
for(int k = 0; k<desc.cols; k++){
v.at<float>(0,128*j+k) = desc.at<float>(j,k);

}
} //now in vector v there are all the descriptors (the problem is that most of them have 0 value)

descriptormat.push_back(v);  //descriptormat is the cv::Mat that I use to train the SVM


Can anyone help me understand how to solve this problem? Many thanks for your help!

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