Hey,
I am working on a detection algorithm and I got a working SVM classifier that uses HOG features calculated with a HOGDescriptor object. If I manually test this classifier (ie. feed it with positive and negative examples that have not been used for training), it classifies them fine. The step where I go from my SVM classifier to the HOGDescriptor is where I assume it goes wrong, it doesn't identify a single object correctly. I have no idea where to start how to debug this..
Here is my code:
https://dl.dropboxusercontent.com/u/40610835/HOG%20Detection.html
Any help is greatly appreciated!
Best regards,
Hans
Update:
To make things clearer so you don't have to go through the entire code, this is how I read in data from the stored SVM classifier:
# load the stored SVM
f = open('model.svm', 'r')
model = yaml.load(f)
rho = model['my_svm']['decision_functions'][0]['rho']
svm_detector = np.array(model['my_svm']['support_vectors'])[0,:]
hog.setSVMDetector(np.hstack([svm_detector, rho]))
There is only one support vector in my classifier, I assumed the SVM weights I need to give to HOGDescriptor.setSVMDetector are these weights with appended the threshold (stored as 'rho' in the yaml file). The HOGDescriptor is initialised as follows :
self.hog = cv2.HOGDescriptor((64,64), (16,16), (8,8), (8,8), 9)
Detection is then simply done using :
locations, weights = hog.detectMultiScale(image)
I didn't quite understand what the parameters in detectMultiScale do exactly and there isn't much documentation regarding their effects. I supplied the SVM with objects of size 64x64 which didn't differ much from the default size of 64x128 so I kept the rest of the parameters the same. I tried 'playing' around with them, but did not achieve much better results.
The summary of my issue now is:
If I manually cut out unseen positive/negative examples, my SVM classifier of OpenCV correctly classifies its labels (using SVM.predict). However if I use this classifier inside a HOGDescriptor with mostly default parameters and use HOGDescriptor.detectMultiScale, I get unexpected results (ie. only a few detections at false locations). I have two assumptions, either my parameters are incorrect or my process of getting the SVM weights from the SVM classifier to the HOGDescriptor is false.
For more information, don't hesitate to ask.
Update2:
After reading through this question, I noticed I need to take the negative of my weights when going from SVM -> HOGDescriptor. This improved my results a lot. I used a very small training set, so I didn't expect it to be flawless, this seems to be what I had expected.
