Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition (2206.13028v1)

Abstract: Graph convolutional networks have been widely used for skeleton-based action recognition due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a local operation, it can only utilize the short-range joint dependencies and short-term trajectory but fails to directly model the distant joints relations and long-range temporal information that are vital to distinguishing various actions. To solve this problem, we present a multi-scale spatial graph convolution (MS-GC) module and a multi-scale temporal graph convolution (MT-GC) module to enrich the receptive field of the model in spatial and temporal dimensions. Concretely, the MS-GC and MT-GC modules decompose the corresponding local graph convolution into a set of sub-graph convolution, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-graph convolutions, and each node could complete multiple spatial and temporal aggregations with its neighborhoods. The final equivalent receptive field is accordingly enlarged, which is capable of capturing both short- and long-range dependencies in spatial and temporal domains. By coupling these two modules as a basic block, we further propose a multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition. The proposed MST-GCN achieves remarkable performance on three challenging benchmark datasets, NTU RGB+D, NTU-120 RGB+D and Kinetics-Skeleton, for skeleton-based action recognition.

• The paper introduces MST-GCN with innovative multi-scale spatial and temporal modules that capture both short- and long-range dependencies.
• It demonstrates enhanced accuracy, achieving up to 91.5% Top-1 accuracy on NTU RGB+D while using fewer parameters than previous models.
• Visualization confirms that MST-GCN effectively focuses on critical body parts to distinguish complex human actions in benchmark datasets.

Overview of "Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition"

The paper "Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition" addresses the limitations in current graph convolutional network (GCN) methodologies for skeleton-based action recognition tasks. The authors propose a new model, the Multi-Scale Spatial Temporal Graph Convolutional Network (MST-GCN), which is designed to enhance the spatial and temporal representation capabilities of skeleton-based data by capturing both short- and long-range dependencies.

Problem Statement and Motivation

Existing GCN models for action recognition often focus on local operations, which effectively capture the relationships between nearby joints in a skeleton, but struggle to model distant connections and long-term temporal dynamics. This shortcoming can lead to reduced accuracy in capturing complex human actions that involve coordination across multiple joints and time frames. The ability to recognize these dependencies is crucial for distinguishing between similar actions and understanding complex movements.

Proposed Solution

To overcome these challenges, the authors introduce two novel modules: the Multi-Scale Spatial Graph Convolution (MS-GC) module and the Multi-Scale Temporal Graph Convolution (MT-GC) module. These modules aim to extend the receptive field without introducing additional parameters, thereby capturing dependencies between joints and frames that are spatially or temporally distant.

• MS-GC Module: This module enhances spatial feature extraction by decomposing traditional spatial graph convolutions into a set of smaller, sub-graph convolutions organized hierarchically. This structure enables each joint to perform multiple spatial aggregations, thus effectively enlarging the receptive field.
• MT-GC Module: Analogous to the MS-GC, the MT-GC module focuses on temporal feature extraction. By extending the spatial approach to the temporal domain, the MT-GC captures long-range temporal dependencies using a similar hierarchical convolution structure.

The combination of these modules within the MST-GCN framework facilitates improved motion pattern learning, leading to more accurate action recognition.

Experimental Results

The MST-GCN model is evaluated on three benchmark datasets: NTU RGB+D, NTU-120 RGB+D, and Kinetics-Skeleton. The model demonstrates significant performance improvements, with enhanced recognition accuracy across all datasets. Key findings include:

• On the NTU RGB+D dataset, MST-GCN achieves a Top-1 accuracy of up to 91.5% under the cross-view protocol, substantially outperforming several state-of-the-art models.
• The MST-GCN model exhibits a clear advantage in terms of parameter efficiency, achieving higher accuracy with fewer model parameters compared to baseline and contemporary approaches.
• Visualization of feature responses indicates that MST-GCN effectively captures relevant spatial and temporal features, focusing on important body parts associated with specific actions.

Implications and Future Directions

The implications of this research are significant for the field of action recognition in AI. By improving the modeling of both spatial and temporal relationships in skeleton data, MST-GCN is able to achieve higher accuracy without increasing computational complexity. This has practical applications in areas such as surveillance, human-computer interaction, and virtual reality.

Looking ahead, the authors suggest further exploration into the joint learning of spatial and temporal features, potentially enhancing performance by simultaneously considering both domains. Furthermore, integrating the model's capabilities with RGB-based methods could lead to robust, multi-modal solutions for action recognition.

Given these contributions, the MST-GCN framework represents a focused step forward in leveraging graph-based methods for the nuanced task of skeleton-based human action recognition, offering a more comprehensive approach to encoding the intricate dynamics of human motion.