OpenCV + HOG +SVM: help needed with SVM single feature vector

I try to implement a people detecting system based on SVM and HOG using OpenCV2.3. But I got stucked.

I came this far: I can compute HOG values from an image database and then I calculate with LIBSVM the SVM vectors, so I get e.g. 1419 SVM vectors with 3780 values each.

OpenCV just wants one feature vector in the method hog.setSVMDetector(). Therefore I have to calculate one feature vector from my 1419 SVM vectors, that LIBSVM has calculated.

I found 开发者_C百科one hint, how to calculate this single feature vector: link

“The detecting feature vector at component i (where i is in the range e.g. 0-3779) is built out of the sum of the support vectors at i * the alpha value of that support vector, e.g. det[i] = sum_j (sv_j[i] * alpha[j]) , where j is the number of the support vector, i is the number of the components of the support vector.”

According to this, my routine works this way: I take the first element of my first SVM vector, multiply it with the alpha value and add it with the first element of the second SVM vector that has been multiplied with alpha value, …

But after summing up all 1419 elements I get quite high values:

16.0657, -0.351117, 2.73681, 17.5677, -8.10134, 
11.0206, -13.4837, -2.84614, 16.796, 15.0564, 
8.19778, -0.7101, 5.25691, -9.53694, 23.9357,

If you compare them, to the default vector in the OpenCV sample peopledetect.cpp (and hog.cpp in the OpenCV source)

0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
-0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
-0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,

you see, that the default vector values are in the boundaries between –1 and +1, but my values exceed them far.

I think, my single feature vector routine needs some adjustment, any ideas?

Regards,

Christoph

The aggregated vector's values do look high.
I used the loadSVMfromModelFile() located in http://lnx.mangaitalia.net/trainer/main.cpp
I had to remove svinstr.sync(); from the code since it caused losing parts of the lines and getting wrong results.
I don't know much about the rest of the file, I only used this function.