Video Imaging Classification

Frames of the video frozen at the frame rate with an iteration on each frame. A Chi-test done for similarity to the first frame at alpha and if so, continue until not so. At that point, store the time in an array and move on until the whole video has passed. At classification, measure the video to each time period for the most similar interval with a Chi-test. After, numpy array the frames as independent variables and numpy array the video as the dependent variable. Output will be a regression with the probability of classification and the time interval.