Depth Human Action Recognition Based on Convolution Neural Networks and Principal Component Analysis

Keyphrases

• Principal Coordinate Analysis (PCoA) 100%
• Recognition-based 100%
• Convolution Neural Network 100%
• Human Action Recognition 100%
• Network Component Analysis 100%
• Convolution Neural Network Model 66%
• Noisy Data 33%
• Video Data 33%
• Algorithm Analysis 33%
• Superior Performance 33%
• Computational Requirements 33%
• Dynamic Information 33%
• Low Dimension 33%
• Problem Recognition 33%
• Convolutional Layer 33%
• Depth Video 33%
• Low Computational 33%
• View-invariant 33%
• Multi-view Video Plus Depth 33%
• Discriminative Representation 33%
• Hidden Dynamics 33%
• View-invariant Feature 33%
• Fourier Temporal Pyramids 33%
• Real Depth 33%
• Nonnegative Tensor 33%
• Viewpoint Variations 33%
• Temporal Misalignment 33%

Computer Science

• Principal Components 100%
• Component Analysis 100%
• Principle Component Analysis 100%
• human action recognition 100%
• Convolutional Neural Network 100%
• Convolution Neural Network Model 100%
• Network Component 100%
• Experimental Result 50%
• Superior Performance 50%
• Dynamic Information 50%
• Recognition Problem 50%
• Convolution Layer 50%
• Invariant 50%