  We propose in this paper a deep-wide network (DWnet) which combines the deep
structure with the broad learning system (BLS) to recognize actions. Compared
with the deep structure, the novel model saves lots of testing time and almost
achieves real-time testing. Furthermore, the DWnet can capture better features
than broad learning system can. In terms of methodology, we use pruned
hierarchical co-occurrence network (PruHCN) to learn local and global
spatial-temporal features. To obtain sufficient global information, BLS is used
to expand features extracted by PruHCN. Experiments on two common skeletal
datasets demonstrate the advantage of the proposed model on testing time and
the effectiveness of the novel model to recognize the action.
