Algorithm for Human Abnormal Behavior Recognition Based on Improved Spatial Temporal Graph Convolutional Networks

With the increasing demand for public safety, the field of abnormal human behavior recognition has undergone significant development. In addressing the low accuracy issue of existing abnormal behavior recognition algorithms due to factors such as environmental influences, changes in viewpoint, and scale variations, this study proposed an improved Spatial temporal graph convolutional network. By incorporating spatial attention and channel attention mechanisms at relevant positions in the network, a dynamic optimization of the skeletal structure graph of the human body was achieved. This ensured that key nodes expressing motion information in the skeletal graph received greater weight values, ultimately improving the accuracy of abnormal behavior classification. To this end, an abnormal behavior dataset was constructed and transformed into skeletal information recognizable by the proposed algorithm using OpenPose. Extensive experiments were conducted on this dataset as well as the large-scale NTU RGB + D dataset using the improved algorithm. The results demonstrate that the algorithm has achieved an increase of approximately 5% in recognition accuracy compared to its pre-improvement state, placing it among the top-performing algorithms in various comparative evaluations.