The following method has been used to attempt to train a classifier to detect fingers:
- Film several hands with fingers open for about 20-30 seconds each at 30 FPS.
- Extract the positive images into set of +/- 3000 bmp images all same size.
- Using bounding box method, create a description file of the positive image sets by selecting a finger to be bound and setting that data to the positive.txt description file.
- Film several scenes/objects that do not contain the object to be detected for negative image sets.
- Extract the negative images into set of +/- 2000 bmp images all same size.
- List the files in a txt description file (ls > negative.txt)
- Pack 3 & 6 into .vec file using opencv_createsamples.
- Begin training classifier.
I have found that this is not accurate.
Is there perhaps a different method of image preparation: - more films? - hundreds of different fingers? - a new bounding box for each finger on a different hand?
Is there something I'm not doing? What is opinion from experts in positive image preparation to get best result?
Thanks.