Hi all
I'm trying to train a classifier to detect cars shadows (rear view) and I am facing difficulties doing that, later I want to use the output to verify the possible car region via HOG+SVM (will worry about it later). I don't know exactly what is needed to do so as I'm using around 692 positive samples with 4400 negatives converted to gray, cropped exactly to the object (160x45). for training I'm using 600 pos, 3500 neg.
My target dimensions: -w 60 -h 17
Positive samples are like (160x45):
Negative samlpes are like (160x45):
And this is the horrible result with only 1 correct catch (I made the rectangle height similar to the width to match the whole car):
What exactly is wrong? Do I need more samples? Is it required to do histogram equalization (before or after)? Is it mandotary to use the annotation tool (I'm using cropped images so my pos.txt having things like pos/pos2.png 1 0 0 160 45)? Do my negative samples should be a full image or just peices matches the size of the positives? I'm so confused!
Appreciate any help really
Thanks
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