MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Human Action Recognition (HAR) in RGB-D videos is a growing field.
• Unimodal approaches (skeleton-based, RGB-based) have advanced significantly.
• Multimodal methods, especially model-level fusion, remain underexplored.

Purpose of the Study:

• To propose a model-based multimodal network (MMNet) for fusing skeleton and RGB data.
• To enhance ensemble recognition accuracy by leveraging complementary information from different modalities.
• To improve the discriminative power of features for HAR.

Main Methods:

• Developed a model-based multimodal network (MMNet).
• Employed a spatiotemporal graph convolution network for skeleton data to learn attention weights.
• Transferred learned attention weights from skeleton to RGB modality network.
• Utilized model-level fusion to combine skeleton and RGB information.

Main Results:

• MMNet outperformed state-of-the-art approaches on six evaluation protocols across five benchmark datasets (NTU RGB+D 60/120, PKU-MMD, Northwestern-UCLA, Toyota Smarthome).
• The method demonstrated consistent performance on the Kinetics 400 RGB video dataset, indicating robustness.
• Achieved effective capture of complementary features between RGB and skeleton modalities.

Conclusions:

• The proposed MMNet effectively fuses skeleton and RGB modalities for improved HAR.
• MMNet provides more discriminative features by capturing complementary information.
• The model-level fusion approach offers a promising direction for multimodal HAR research.