Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention (1907.08871v1)

Abstract: We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand skeleton, where the node features and edges are then automatically learned via a self-attention mechanism that performs in both spatial and temporal domains. We further propose to leverage the spatial-temporal cues of joint positions to guarantee robust recognition in challenging conditions. In addition, a novel spatial-temporal mask is applied to significantly cut down the computational cost by 99%. We carry out extensive experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior performance of our method compared with the state-of-the-art methods. The source code can be found at https://github.com/yuxiaochen1103/DG-STA.

• The paper introduces the DG-STA model that dynamically constructs graphs via spatial-temporal attention, enabling adaptive and accurate hand gesture recognition.
• The model leverages a self-attention mechanism to learn optimal node features and edges while reducing computational overhead by 99% through spatial-temporal masks.
• Experimental results on DHG-14/28 and SHREC'17 datasets show superior performance with accuracies of 91.9% for 14 gestures and 88.0% for 28 gestures.

Overview of Constructing Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

The paper, titled "Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention," introduces a novel method for recognizing hand gestures using dynamic graphs. The authors propose the Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) model which addresses limitations of previous skeleton-based hand gesture recognition approaches. This work positions itself within the significant field of gesture recognition crucial for applications such as human-computer interaction and sign language interpretation.

Methodological Innovations

The DG-STA model introduces several key innovations:

1. Dynamic Graph Construction: Unlike conventional methods using predetermined graph structures, this approach constructs dynamic graphs where node features and edges are learned through a self-attention mechanism. This allows for flexible adaptation to the spatial-temporal variations inherent in different hand gestures.
2. Spatial-Temporal Attention Mechanism: Incorporating a dual-domain attention mechanism in both spatial and temporal dimensions provides robustness and specificity in recognizing gestures, addressing the common problem of inefficient feature exploitation in prior techniques. Spatial attention focuses on the interactions among hand joints at any single frame while temporal attention addresses how these features evolve over consecutive frames.
3. Efficiency Optimization via Masking: The computational overhead typically associated with dynamic graph models is significantly reduced through the implementation of spatial-temporal masks, cutting computational costs by 99%. This efficiency gain is crucial for real-time processing and application scalability.

Experiments and Results

The authors conducted extensive experiments on two standard benchmarks: DHG-14/28 and SHREC'17 datasets. These datasets are chosen for their representation of rigorous testing conditions involving various hand gestures captured through depth cameras. The DG-STA model showed superior performance over state-of-the-art methods, achieving impressive accuracies (91.9% for 14 gestures and 88.0% for 28 gestures on the DHG dataset). The empirical validations underscore its potential utility and robustness compared to existing techniques, demonstrating the efficacy of the dynamic graph-based approach.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, the DG-STA method enables more accurate and efficient hand gesture recognition. This is highly beneficial for interactive systems where nuanced and responsive interpretations of gestures are required. Theoretically, the approach suggests a new paradigm in gesture recognition—shifting from static to dynamic models further refined by attention mechanisms. This methodology could influence future works in related fields such as human action recognition and robotics.

Looking ahead, there are several promising directions for future research. The authors could extend this model to handle more complex gestures involving multiple hands or integrate contextual information from surrounding areas for a more holistic gesture interpretation. Additionally, advancements in dynamic graph learning could pave the way for applications beyond gesture recognition, in areas such as cognitive interaction modeling and behavioral analysis.

In conclusion, the introduction of the DG-STA model is a significant contribution to the field of skeleton-based gesture recognition, offering not only enhanced performance but also paving the way for future exploration into dynamic modeling and attention-based frameworks.