  Human activity recognition is an emerging and important area in computer
vision which seeks to determine the activity an individual or group of
individuals are performing. The applications of this field ranges from
generating highlight videos in sports, to intelligent surveillance and gesture
recognition. Most activity recognition systems rely on a combination of
convolutional neural networks (CNNs) to perform feature extraction from the
data and recurrent neural networks (RNNs) to determine the time dependent
nature of the data. This paper proposes and designs two transformer neural
networks for human activity recognition: a recurrent transformer (ReT), a
specialized neural network used to make predictions on sequences of data, as
well as a vision transformer (ViT), a transformer optimized for extracting
salient features from images, to improve speed and scalability of activity
recognition. We have provided an extensive comparison of the proposed
transformer neural networks with the contemporary CNN and RNN-based human
activity recognition models in terms of speed and accuracy.
