Improving Dynamic Gesture Recognition with Attention-Enhanced LSTM and Grounding SAM
Jinlong Chen, Fuqiang Jin, Yingjie Jiao, Yongsong Zhan, Xingguo Qin · Electronics · 2025
Dynamic gesture detection is a key topic in computer vision and deep learning, with applications in human–computer interaction and virtual reality. However, traditional methods struggle with long sequences, complex scenes, and multimodal data, facing issues such as high computational cost and background noise. This study proposes an Attention-Enhanced dual-layer LSTM (Long Short-Term Memory) network combined with Grounding SAM (Grounding Segment Anything Model) for gesture detection. The dual-layer LSTM captures long-term temporal dependencies, while a multi-head attention mechanism improves the extraction of global spatiotemporal features. Grounding SAM, composed of Grounding DINO for object localization and SAM (Segment Anything Model) for image segmentation, is employed during preprocessing to precisely extract gesture regions and remove background noise. This enhances feature quality and reduces interference during training. Experiments show that the proposed method achieves 96.3% accuracy on a self-constructed dataset and 96.1% on the SHREC 2017 dataset, outperforming several baseline methods by an average of 4.6 percentage points. It also demonstrates strong robustness under complex and dynamic conditions. This approach provides a reliable and efficient solution for future dynamic gesture-recognition systems.