Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition
This work addresses action recognition for applications like surveillance or human-computer interaction, but it is incremental as it adapts existing 3D CNN techniques to a new domain.
The paper tackled the challenge of efficiently extracting spatial-temporal information from skeleton sequences for 3D human action recognition by proposing a novel two-stream model using 3D CNN, and it demonstrated superior performance over most RNN-based methods on datasets like SmartHome and NTU RGB-D.
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. Our method consists of three stages. First, skeleton joints are mapped into a 3D coordinate space and then encoding the spatial and temporal information, respectively. Second, 3D CNN models are seperately adopted to extract deep features from two streams. Third, to enhance the ability of deep features to capture global relationships, we extend every stream into multitemporal version. Extensive experiments on the SmartHome dataset and the large-scale NTU RGB-D dataset demonstrate that our method outperforms most of RNN-based methods, which verify the complementary property between spatial and temporal information and the robustness to noise.