Basketball action recognition algorithm based on global context awareness
Lijun He, Jiangtao Luo, Guoliang Xu · 2023
ognition in simple scenes, the difference between classes of basketball actions is slight, and the backgrounds in the video are very similar. Therefore, it is not easy to recognize the basketball actions directly based on short-term temporal information or the scene information in the video. A Global Context-Aware Network (GCA-Net) for basketball action recognition is proposed to address this problem in this paper. It contains a Multi-Time Scale Aggregation (MTSA) module and a Spatial-Channel Interaction (SCI) module to process multiple types of information on feature layers. The MTSA module uses a temporal pyramid to get contextual links in the temporal dimension through one-dimensional convolution with different dilation rates. The SCI module enhances the feature representation to obtain more prosperous category attributes and spatial information by interacting with information across dimensions. We conducted experiments on the basketball action recognition dataset SpaceJam, and the results show that GCA-Net can effectively classify basketball actions. The average recognition accuracy of ten types of basketball actions in the dataset is 91.54%, which is an improvement compared with the current mainstream methods.