How to training HOG and use my HOGDescriptor?

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Hi! I have found this repository:

https://github.com/DaHoC/trainHOG

It seems to be a nice tutorial on how to train an HOG detector using SVMlight 6.02. Even if I haven't tried myself I would give it a try!

I have trained SVMlite with data, that I have generated with opencv, and get the model file. After that with weightVector.pl got the weighted vector, but when run hog detector with that the result is awful... It detects some random images or only one in the center of the image... ;/

In OpenCV, there's undocumented API

HOGDescriptor::compute(const cv::Mat &img, vector<float> &descriptors)

which you could pass in your image, and retrieve the descriptors for it. And then you could construct a large feature matrix containing both positive and negative features, and also label matrix that specifies which entry in that feature matrix is positive or negative.

Both the features and label matrices then could be passed into CvSVM class for training, and you could save it in HDD and load it afterwards. Later on you could extract features from a patch of an image, use compute to extract the features, and pass it into svm.predict to obtain the prediction whether it's positive or negative image. The downside from this is that you need to apply your own detection window algorithm, etc.

Below is some sample code to train your own SVM.

Mat labels( pos_count + neg_count, 1, CV_32FC1, Scalar(-1.0) );
labels.rowRange( 0, pos_count ) = Scalar( 1.0 );

CvSVM svm;
CvSVMParams params;
params.C = c;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria( CV_TERMCRIT_ITER, 10000, 1e-6 );

Mat feat_matrix( pos_count + neg_count, pos_feats[0].cols, pos_feats[0].type() );
for( int i = 0; i < pos_count; i++ )
    pos_feats[i].copyTo( feat_matrix.rowRange(i, i+1) );
for( int i = 0; i < neg_count; i++ )
    neg_feats[i].copyTo( feat_matrix.rowRange(i + pos_count, i + pos_count + 1) );

cout << "Start training SVM" << endl;
svm.train( feat_matrix, labels, Mat(), Mat(), params );
svm.save( (path + svm_file).c_str() );

You need to get the HOG features from all of your positive and negative sample images using HOGDescriptor:compute functions, then feed the result to SVM library such as SVMlight. This page will help you to compute the feature and get the resulting model from SVM Light. The model will be available in genfiles/descriptorVector