I have a series of Neural Networks that I run on some video data. First network detects bounding boxes, then it extracts features for object tracking, matching each box to an id frame by frame and finally some other CNN is applied to all "boxes" extracted.
Naturally this is very slow and I understand that this is just so much you could do, but I wonder if there's a way to optimise this for speed?
Some suggestions I've heard is to concatenate my networks. I've used Thread class with Queue with some effect but want to try something a bit more radical