用词嵌入增强骨骼动作识别的语义信息，提升复杂场景下的识别效果。
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition
- 用词嵌入替代独热编码，构建语义空间表示关节与物体关系
- 在多个装配数据集上显著提升分类准确率，支持多种骨骼类型和物类
- 适合工业4.0中需理解人机交互语义的协作机器人场景
有效的人体动作识别广泛应用于工业4.0中的协作机器人，以辅助装配任务。然而，传统基于骨骼的方法常丢失关键点语义，限制了其在复杂交互中的表现。本文提出一种新颖的骨架动作识别方法，通过引入词嵌入来编码语义信息，丰富输入表示。该方法将原本的独热编码替换为语义体积，使模型能够捕捉关节与物体之间的有意义关联。在多个装配数据集上的大量实验表明，该方法显著提升了分类性能，并同时支持不同骨架类型和物类，增强了泛化能力。研究结果凸显了融合语义信息对提升动态多样环境中骨架动作识别潜力的重要性。
原文摘要 · Abstract (English)
Effective human action recognition is widely used for cobots in Industry 4.0 to assist in assembly tasks. However, conventional skeleton-based methods often lose keypoint semantics, limiting their effectiveness in complex interactions. In this work, we introduce a novel approach to skeleton-based action recognition that enriches input representations by leveraging word embeddings to encode semantic information. Our method replaces one-hot encodings with semantic volumes, enabling the model to capture meaningful relationships between joints and objects. Through extensive experiments on multiple assembly datasets, we demonstrate that our approach significantly improves classification performance, and enhances generalization capabilities by simultaneously supporting different skeleton types and object classes. Our findings highlight the potential of incorporating semantic information to enhance skeleton-based action recognition in dynamic and diverse environments.
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