山东理工大学学报（自然科学版）2026，Vol.40Issue(5)：35-41,7.
基于频率感知混合Transformer的加注动作识别方法研究
Research on refueling action recognition method based on frequency-aware hybrid Transformer model
摘要
Abstract
In recent years,Transformer models have demonstrated significant advantages in modeling long-term dependencies within skeleton sequences,especially in the domain of skeleton action recogni-tion.However,current Transformer-based methods typically rely on simplistic attention mechanisms to capture spatial-temporal features,which limits their ability to learn discriminative representations for ac-tions with similar motion patterns.To address these challenges,this study introduces a frequency-aware mixed Transformer model for skeleton action recognition.This model is specifically designed to recognize similar skeleton actions with subtle discriminative movements by combining frequency-domain modeling with spatial-temporal attention mechanisms.By integrating high-frequency and low-frequency branch modules,the model enables multi-scale feature interaction,thereby enhancing its ability to distinguish similar actions.Comparative experiments between the proposed model and five mainstream models were conducted on a self-built skeleton action dataset containing seven types of standard operations.The results show that the frequency-aware mixed Transformer model achieves a recognition accuracy of 97.62%,out-performing other skeleton action recognition models.关键词
骨架动作识别/加注动作/频域建模/时空注意力机制/TransformerKey words
skeleton action recognition/filling action/frequency domain modeling/spatial-temporal attention mechanism/Transformer分类
矿业与冶金引用本文复制引用
吴夏,章平,王淼,鲁鹏飞,时雪峰,刘涛..基于频率感知混合Transformer的加注动作识别方法研究[J].山东理工大学学报（自然科学版）,2026,40(5):35-41,7.基金项目
安徽省高校科研项目(2024AH050109) （2024AH050109）
安徽未来技术研究院企业合作项目(2023qyhz12) （2023qyhz12）
