Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition

Understand

It remains a challenge to efficiently extract spatialtemporal information from skeleton sequences for 3D human action recognition.

• Although most recent action recognition methods are based on Recurrent Neural Networks which present outstanding performance, one of the shortcomings of these methods is the tendency to overemphasize the temporal information.
• Since 3D convolutional neural network(3D CNN) is a powerful tool to simultaneously learn features from both spatial and temporal dimensions through capturing the correlations between three dimensional signals, this paper proposes a novel two-stream model using 3D CNN.
• To our best knowledge, this is the first application of 3D CNN in skeleton-based action recognition.