PoseConv3D-TAFI:Skeleton Action Recognition Using Frame Interrelations in Conv3D

In skeleton-based action recognition, accurately recognizing human actions is challenging due to complex movements, keypoints noise and action similarity. This paper introduces the PoseConv3D-Tafimodel, which combines PoseConv3D with the new TSCA, IFA modules and an integrated learning strategy. The TSCA module improves the temporal modeling of joints and the recognition of complex actions. The IFA module exploits inter-frame differences to distinguish similar actions better. The integrated learning strategy improves the robustness and generalization of the model. Experiments show that PoseConv3D-TAFI improves the top-1 accuracy by 1.4% and 2.02% on the UCF101 and HMDB51 datasets, respectively, and outperforms existing methods in handling complex and similar actions. The model shows strong potential for practical applications of skeleton-based action recognition.