Skeleton‐based action recognition with JRR‐GCN
Fanfan Ye, Huiming Tang · Electronics Letters · 2019
A novel joints relation‐reasoning, graph convolutional network (JRR‐GCN) is proposed to solve the problem of skeleton‐based action recognition (SAR). Different from the conventional spatial convolutional network‐based methods, the adjacency matrices of JRR‐GCN is reasoned by joints relation‐reasoning network (JRR) automatically, which results in generating a more realistic representation of skeleton topology and yields better adjacency matrices for every sample. JRR is trained with the reinforcement learning with a novel state‐action mapping scheme. Extensive experiments are conducted on two public SAR datasets, NTU‐RGB+D and kinetics. Also the obtained results demonstrate the effectiveness of JRR‐GCN comparing with the state‐of‐the‐art SAR methods.