There's a really straightforward approach to turning video into feature vectors which you can readily plug into any old ML algorithm. You can turn every decoded frame (which is essentially a PNG, right?) into a (Width*Height)x1 vector, where each cell is an RGB pixel. You can then compose these vectors into a matrix, or perform further operations on them, e.g. SVD. Whether or not this is a good approach, though, will depend of course on your application. For face detection, for example, this is not adequate, and you'll need more sophisticated algorithms. For a good overview of that, see Jason Lawrence's slides[1] and also take a look at some of the other stuff he covered in his Computer Vision course[2].
Thanks for pointers! I'm basically below/at 101 with ML, but have background in computer graphics.
I was wondering if one could utilise ML for either or both of two things: object outline spatial/temporal (feathering can solve for motion blur) and better Chroma key.
Sorry if that's too basic for what you were asking. If you want to see some messy code that does this using OpenCV, here's some I wrote a while back with a friend, starting on line 127: https://github.com/sprestwood/CompVisionS2015/blob/master/te...
[1] http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/le...
[2] http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/sc...