ST-CGNet: A spatiotemporal gesture recognition network with triplet attention and dual feature fusion

Gesture recognition, as a critical area in human–computer interaction, faces significant challenges in modeling complex spatiotemporal dynamics and adapting to gesture diversity. This paper proposes a novel framework—ST-CGNet, which captures multi-scale spatiotemporal features by integrating an optimized C3D network with a lightweight GatedConvLSTM. The C3D module focuses on short-term spatiotemporal feature extraction, while the GatedConvLSTM captures long-term dependencies through a gating mechanism. To enhance sensitivity to dynamic variations in gestures, a TripletAttention3D module is introduced, which strengthens the model’s ability to focus on salient motion patterns. Additionally, an adaptive fusion strategy is employed to dynamically weight and integrate features from both branches, improving performance across diverse gesture types. Experiments on the Jester and EgoGesture datasets demonstrate that the proposed method significantly outperforms baseline models in terms of recognition accuracy and generalization, particularly in handling complex gesture sequences. These results highlight the effectiveness of the proposed approach as a promising solution for dynamic gesture recognition.