Optimized Temporal Convolutional Networks with Multi-Stage Attention for Efficient Human Action Recognition

The advancement of computer vision applications, especially in the areas of surveillance, healthcare, and human-computer interaction, depends heavily on human action recognition. Despite its effectiveness, traditional temporal convolutional networks (TCNs) frequently fall short in capturing intricate temporal dynamics and prioritising crucial action segments. In order to overcome these constraints, this study introduces a unique Optimized Temporal Convolutional Network (OTCN) architecture that incorporates a multi-stage attention mechanism. The hierarchical attention module ensures improved representation at a lower computing cost by dynamically identifying and amplifying important features across a range of temporal resolutions. Comprehensive tests on several benchmark datasets show that OTCN is perfect for real-time applications since it not only achieves high accuracy but also provides computing efficiency. Our findings demonstrate how multi-stage attention can revolutionise the development of reliable, scalable, and effective human action recognition systems.