Scholarlink

<p>Interactive action recognition aims to automatically recognize and understand behaviors involving interactions between humans and objects. In this paper, we propose a multi-stream graph neural network approach that efficiently recognizes interactive actions from skeletal motion sequences. Our method consists of three targeted deep neural networks: the Priori Interaction Graph Convolutional Network (PI-GCN), the Dynamic Interaction Hypergraph Convolutional Network (DIH-GCN), and the Global Interaction Attention (GI-Attention). PI-GCN improves feature aggregation by incorporating potential interactions into the skeletal adjacency matrix. DIH-GCN dynamically constructs hypergraphs using the K-nearest neighbor algorithm to capture local interaction features. GI-Attention integrates attention computation across spatial and temporal dimensions to capture global interaction features. Experiments on the NTU-RGB+D 120 interaction dataset demonstrate the superiority of our model, achieving state-of-the-art performance. Our work highlights the importance of modeling dynamic interaction information for effective interactive action recognition. The source code is accessible via <a href="https://github.com/HENRYDJ520/DM-GCN">https://github.com/HENRYDJ520/DM-GCN</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic multi-stream graph neural networks for efficient interactive action recognition

  • 期刊论文

  • 发表日期：

    2025年6月28日

查看全文
  • Hanrui Jiang,
  • Tuo Zang,
  • Wenwen Zhang,
  • Siyu Li,
  • Wenqiang Wang,
  • Lingfeng Liu

摘要

Interactive action recognition aims to automatically recognize and understand behaviors involving interactions between humans and objects. In this paper, we propose a multi-stream graph neural network approach that efficiently recognizes interactive actions from skeletal motion sequences. Our method consists of three targeted deep neural networks: the Priori Interaction Graph Convolutional Network (PI-GCN), the Dynamic Interaction Hypergraph Convolutional Network (DIH-GCN), and the Global Interaction Attention (GI-Attention). PI-GCN improves feature aggregation by incorporating potential interactions into the skeletal adjacency matrix. DIH-GCN dynamically constructs hypergraphs using the K-nearest neighbor algorithm to capture local interaction features. GI-Attention integrates attention computation across spatial and temporal dimensions to capture global interaction features. Experiments on the NTU-RGB+D 120 interaction dataset demonstrate the superiority of our model, achieving state-of-the-art performance. Our work highlights the importance of modeling dynamic interaction information for effective interactive action recognition. The source code is accessible via https://github.com/HENRYDJ520/DM-GCN.