Hello, I am trying to train cascade to detect motorcycle. I tried LBP (with stage 15) and it's giving too much false negatives, Here is what I did till now,
I recorded required traffic videos . Developed a program to track all moving objects and when it crosses line of detection, cv::imwrite() to save the Rectangular ROI. Manually separated which are Motor Cycles and which are not, cropped them and made sure that the others donot have any motorcycles. Like this I have collected around 257 positive images and 653 negative images (I can get more negatives if required),
If you would like to have a look at the collected data, I have uploaded them here :
- positive images (all) (ignore the names of the images)
- negative images
It looks like this :
positives :
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negatives are cars, trucks, people, etc...
I generated LBP cascade using opencv_traincascade with numStages 15 and 20 . They are giving too much (around 55%) false negatives
I tried to generate HOG cascade using https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp , but it requires be to provide samples of fixed sizes , that too 64x128 (which will totally deform my images, I don't think that's going to work) .
By seeing the Images above, could you please suggest some ideas how I achieve my requirement ? Should I try HOG ? HAAR ? LBP ? should I increase/decrease the numStages ?
Your inputs are most valued, Thanks.