Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning

Abstract: Skeleton-based action recognition has made great progress recently, but many problems still remain unsolved. For example, most of the previous methods model the representations of skeleton sequences without abundant spatial structure information and detailed temporal dynamics features. In this paper, we propose a novel model with spatial reasoning and temporal stack learning (SR-TSL) for skeleton based action recognition, which consists of a spatial reasoning network (SRN) and a temporal stack learning network (TSLN). The SRN can capture the high-level spatial structural information within each frame by a residual graph neural network, while the TSLN can model the detailed temporal dynamics of skeleton sequences by a composition of multiple skip-clip LSTMs. During training, we propose a clip-based incremental loss to optimize the model. We perform extensive experiments on the SYSU 3D Human-Object Interaction dataset and NTU RGB+D dataset and verify the effectiveness of each network of our model. The comparison results illustrate that our approach achieves much better results than state-of-the-art methods.

• The paper introduces SR-TSL, combining a Spatial Reasoning Network and a Temporal Stack Learning Network to enhance skeleton-based action recognition.
• It utilizes a Residual Graph Neural Network for decomposing body parts and a skip-clip LSTM to capture both short- and long-term temporal dependencies.
• Experimental results show 84.8% and 92.4% accuracy on NTU datasets, demonstrating significant improvements over previous methods in practical applications.

Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning

This paper presents a sophisticated approach for skeleton-based action recognition, addressing ongoing challenges in capturing both spatial structure information and temporal dynamics in human motion data. The proposed model, termed SR-TSL (Spatial Reasoning and Temporal Stack Learning), integrates two key components: a Spatial Reasoning Network (SRN) and a Temporal Stack Learning Network (TSLN). This approach is rigorously evaluated on the NTU RGB+D and SYSU 3D Human-Object Interaction datasets, demonstrating superior performance over existing state-of-the-art methods.

The Spatial Reasoning Network (SRN) distinguishes itself by utilizing a Residual Graph Neural Network (RGNN) to analyze the spatial structural relationships of skeleton frames. By decomposing the human body into multiple parts, such as arms, legs, and trunk, the SRN harnesses a graph-based model where each node represents a body part. This setup facilitates the encoding of complex spatial dependencies, achieved through successive message-passing steps. The paper shows that with only moderate increases in time steps (up to T=3), this network can effectively learn high-level spatial information, which becomes foundational for subsequent temporal processing.

The Temporal Stack Learning Network (TSLN) component innovatively addresses temporal dynamics using a skip-clip LSTM architecture. Here, the sequence is divided into contiguous clips, allowing the model to capture short-term dependencies before stacking these into a long-term temporal representation. The results underscore the importance of temporal granularity and continuity, indicated by performance gains when suitably segmenting the clips (optimal results being around d=10 frames per clip).

A notable contribution of this work is the introduction of a clip-based incremental loss, which emphasizes the temporal importance of sequential clips, enhancing convergence speed and overall recognition performance. This loss function effectively stresses the importance of intermediate temporal details over extended sequences, reinforcing learning from both position and velocity data streams.

Experimental evaluations attest to the model's efficacy. The SR-TSL achieves accuracy rates of 84.8% (Cross-Subject) and 92.4% (Cross-View) on the NTU dataset, marking significant improvements over previous approaches. Meanwhile, results on the SYSU dataset highlight its robustness across different splits. The incorporation of a two-stream network architecture further distinguishes the proposed method, effectively utilizing positional and differential velocity information which collectively elevate its action recognition capabilities.

From a practical perspective, this model holds substantial promise in domains requiring precise human motion understanding, such as surveillance, sports analytics, and human-computer interaction. Theoretically, the integration of residual graph structures with deep temporal learning represents a pivotal step forward in spatial-temporal data processing. Looking ahead, potential enhancements could involve deeper architectural modifications or adapting the method to consider additional environmental or contextual factors prevalent in multi-agent interactions.

In conclusion, the SR-TSL model significantly advances the field of skeleton-based action recognition by aptly balancing spatial and temporal analyses through well-crafted neural network structures and loss functions. Further research inspired by this work could explore expanded datasets or cross-modal integrations to further harness the potential of such a sophisticated framework.