When it comes to neural networks learning motion, it's all relative

Seeking to explore the capabilities of neural networks for recognizing and predicting motion, a group of researchers led by Hehe Fan developed and tested a deep learning approach based on relative change in position encoded as a series of vectors, finding that their method worked better than existing frameworks for modeling motion. The group's key innovation was to encode motion separately from position. The group's research was published in Intelligent Computing. The new method, VecNet LSTM, scored higher than six other artificial neural network frameworks within the field of video research when tested on recognition of motion. Some of the other frameworks were merely weaker, while others were totally unsuitable for modeling motion.