Multiscaled Multi-Head Attention-based Video Transformer Network for Hand Gesture Recognition
This addresses gesture recognition challenges for human-computer interaction, but it appears incremental as it builds on existing transformer and attention methods.
The paper tackled dynamic hand gesture recognition by proposing a Multiscaled Multi-Head Attention Video Transformer Network (MsMHA-VTN), achieving accuracies of 88.22% on NVGesture and 99.10% on Briareo datasets.
Dynamic gesture recognition is one of the challenging research areas due to variations in pose, size, and shape of the signer's hand. In this letter, Multiscaled Multi-Head Attention Video Transformer Network (MsMHA-VTN) for dynamic hand gesture recognition is proposed. A pyramidal hierarchy of multiscale features is extracted using the transformer multiscaled head attention model. The proposed model employs different attention dimensions for each head of the transformer which enables it to provide attention at the multiscale level. Further, in addition to single modality, recognition performance using multiple modalities is examined. Extensive experiments demonstrate the superior performance of the proposed MsMHA-VTN with an overall accuracy of 88.22\% and 99.10\% on NVGesture and Briareo datasets, respectively.