Well, I post the same question in the main stack before finding the right place, sorry.
A friend of mine is working with more than a 100 videos as sample for his neural network. Each video last more than a couple of minutes with around 24 frames per second. The objective, using deep learning, is to detect movement through all the samples.
The problem for him is the quantity of data he is dealing with. The training part require/consumes too much time. I'm no expert with data preparation, but I thought maybe he could turn all frame into dataframe, clean them from mono color image (full black/white), turn them into gray instead of full rgb and compress them but, I'm not sure if it will be enough.
Do you think of better method to reduce the training sample?