Yeah I know, best title ever. Anyway,
I want to make a neural network which is fed with frames coming from an usb camera. Don't wanna be so specific, so I'm just gonna say that the network's goal is to classify human hand gestures, therefore I need to make sure it can effectively learn how the hand moves around.
My problem is that I've no idea about what happens when having 3 channels instead of 1, I only know that (for 3 channels) it does 3 separate convolution operations with the same kernel, resulting actually in 3 separate layers. How do this 3 channels affect the network? Does it learn from the movement 3 parallel times, then it mixes toghether this 3 "separate movements"? Do I need to make it single channel to help him detect the hand?
PS: the text is problably confusing, but that's because I'm confused to, that's why I'm asking.