A dual-stream spatial-temporal detector for action recognition

In response to the issue that existing human action recognition models can not make full use of complementary information from different modalities, this thesis proposes a multi-path attention module MA to form the MA-GCN model. modalities, this thesis proposes a dual-stream human action recognition model SRHAR that fuses skeleton data and RGB data. This model utilizes LAF proposed in this thesis to fuse skeleton features and RGB features. The introduction of skeleton modality enables the RGB modality to obtain the RGB features. The introduction of skeleton modality enables the RGB modality to obtain complementary information, resulting in more accurate prediction results. This algorithm focuses more on recognition accuracy and has a slower recognition speed, but achieves leading performance in terms of accuracy on public datasets.