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

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]. 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/test_scripts/people_detector.py#L127 https://github.com/sprestwood/CompVisionS2015/blob/master/te... [1] http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/lectures/lecture07_recognition.pdf http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/le... [2] http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/schedule.html http://www.cs.virginia.edu/~gfx/Courses/2015/TopicsVision/sc...