  Online action recognition is an important task for human centered intelligent
services, which is still difficult to achieve due to the varieties and
uncertainties of spatial and temporal scales of human actions. In this paper,
we propose two core ideas to handle the online action recognition problem.
First, we combine the spatial and temporal skeleton features to depict the
actions, which include not only the geometrical features, but also multi-scale
motion features, such that both the spatial and temporal information of the
action are covered. Second, we propose a memory group sampling method to
combine the previous action frames and current action frames, which is based on
the truth that the neighbouring frames are largely redundant, and the sampling
mechanism ensures that the long-term contextual information is also considered.
Finally, an improved 1D CNN network is employed for training and testing using
the features from sampled frames. The comparison results to the state of the
art methods using the public datasets show that the proposed method is fast and
efficient, and has competitive performance
