Skeletal Action Recognition

Skeletal action recognition focuses on automatically identifying human actions from sequences of skeletal joint positions, aiming to build robust and efficient systems for applications like human-computer interaction and autonomous driving. Current research emphasizes improving model accuracy and efficiency through innovative architectures, including graph convolutional networks (GCNs), transformers, and spiking neural networks (SNNs), often incorporating techniques like frequency analysis and multi-scale feature extraction to better capture temporal and spatial dynamics. These advancements are driving progress towards more accurate and resource-efficient action recognition systems, with implications for various fields including healthcare, robotics, and virtual reality.