  Action recognition is a critical task for social robots to meaningfully
engage with their environment. 3D human skeleton-based action recognition is an
attractive research area in recent years. Although, the existing approaches are
good at action recognition, it is a great challenge to recognize a group of
actions in an activity scene. To tackle this problem, at first, we partition
the scene into several primitive actions (PAs) based upon motion attention
mechanism. Then, the primitive actions are described by the trajectory vectors
of corresponding joints. After that, motivated by text classification based on
word embedding, we employ convolution neural network (CNN) to recognize
activity scenes by considering motion of joints as "word" of activity. The
experimental results on the scenes of human activity dataset show the
efficiency of the proposed approach.
