Question

I am learning to implement a hand gesture recognition project. For this, I have gone through several tutorials where they use color information, background subtraction, various object segmentation techniques.

However, one that I would like to use is a method using cascading classifiers however I dont have much understanding in this approach. I have read several text and papers and I understand its theory however, I still dont understand what are good images to train the cascading classifer on. Is it better to train it on natural color images or images with hand gestures processed with canny edge detection or some other way.

Also, is there any method that uses online training and testing methods similar to openTLD but where the steps are explained. The openCV documentation for 2.3-2.4.3 are incomplete with respect to the machine learning and object recognition and tracking except for the code available at: http://docs.opencv.org/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html

I know this is a long question but I wanted to explain my problem thoroughly. It would help me to understand the concept better than just to use online code.

Sincere thanks in advance!

Answer 1

if you think about haar classifier, a good tutorial is here