An Investigation of GCN-based Human Action Recognition Using Skeletal Features

Keyphrases

• Network Applications 100%
• Human Action Recognition 100%
• Skeletal Phenotype 100%
• Graph Convolutional Network 100%
• Feature-based 40%
• Development Framework 20%
• Benchmark Dataset 20%
• Performance Enhancement 20%
• Computer Vision 20%
• Optical Flow 20%
• Network Approach 20%
• Large-scale Dataset 20%
• Expressive Power 20%
• Deep Learning Methods 20%
• Data Modality 20%
• Skeleton-based 20%
• Operational Logic 20%
• Motion Dynamics 20%
• Illumination Variation 20%
• Video Flow 20%
• Attractive Area 20%
• Scene Variation 20%
• Skeleton Data 20%

Computer Science

• human action recognition 100%
• Graph Convolutional Network 100%
• Learning Approach 20%
• Future Direction 20%
• Development Framework 20%
• Deep Learning Method 20%
• Computer Vision 20%
• Performance Enhancement 20%
• Convolutional Neural Network 20%
• Expressive Power 20%
• Recurrent Neural Network 20%
• Benchmarking Dataset 20%
• Conventional Research 20%