  When the input to a deep neural network (DNN) is a video signal, a sequence
of feature tensors is produced at the intermediate layers of the model. If
neighboring frames of the input video are related through motion, a natural
question is, "what is the relationship between the corresponding feature
tensors?" By analyzing the effect of common DNN operations on optical flow, we
show that the motion present in each channel of a feature tensor is
approximately equal to the scaled version of the input motion. The analysis is
validated through experiments utilizing common motion models. %These results
will be useful in collaborative intelligence applications where sequences of
feature tensors need to be compressed or further analyzed.
