  This paper investigates body bones from skeleton data for skeleton based
action recognition. Body joints, as the direct result of mature pose estimation
technologies, are always the key concerns of traditional action recognition
methods. However, instead of joints, we humans naturally identify how the human
body moves according to shapes, lengths and places of bones, which are more
obvious and stable for observation. Hence given graphs generated from skeleton
data, we propose to develop convolutions over graph edges that correspond to
bones in human skeleton. We describe an edge by integrating its spatial
neighboring edges to explore the cooperation between different bones, as well
as its temporal neighboring edges to address the consistency of movements in an
action. A graph edge convolutional neural network is then designed for skeleton
based action recognition. Considering the complementarity between graph node
convolution and graph edge convolution, we additionally construct two hybrid
neural networks to combine graph node convolutional neural network and graph
edge convolutional neural network using shared intermediate layers.
Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our
graph edge convolution is effective to capture characteristic of actions and
our graph edge convolutional neural network significantly outperforms existing
state-of-art skeleton based action recognition methods. Additionally, more
performance improvements can be achieved by the hybrid networks.
