  Designing a scheme that can achieve a good performance in predicting single
person activities and group activities is a challenging task. In this paper, we
propose a novel robust and efficient human activity recognition scheme called
ReHAR, which can be used to handle single person activities and group
activities prediction. First, we generate an optical flow image for each video
frame. Then, both video frames and their corresponding optical flow images are
fed into a Single Frame Representation Model to generate representations.
Finally, an LSTM is used to pre- dict the final activities based on the
generated representations. The whole model is trained end-to-end to allow
meaningful representations to be generated for the final activity recognition.
We evaluate ReHAR using two well-known datasets: the NCAA Basketball Dataset
and the UCFSports Action Dataset. The experimental results show that the pro-
posed ReHAR achieves a higher activity recognition accuracy with an order of
magnitude shorter computation time compared to the state-of-the-art methods.
