Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition

arXiv:2404.19383v1 Announce Type: new
Abstract: Human action video recognition has recently attracted more attention in applications such as video security and sports posture correction. Popular solutions, including graph convolutional networks (GCNs) that model the human skeleton as a spatiotemporal graph, have proven very effective. GCNs-based methods with stacked blocks usually utilize top-layer semantics for classification/annotation purposes. Although the global features learned through the procedure are suitable for the general classification, they have difficulty capturing fine-grained action change across adjacent frames …

abstract action recognition applications arxiv attention block convolutional cs.cv fine-grained graph human networks popular posture recognition security semantic solutions sports type video