Action recognition algorithm based on skeleton graph with multiple features and improved adjacency matrix

Autor: Shanqing Zhang, Shuheng Jiao, Yujie Chen, Jiayi Xu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Image Processing, Vol 18, Iss 13, Pp 4250-4262 (2024)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.13245
Popis: Abstract Although graph convolutional networks have achieved good performances in skeleton‐graph‐based action recognition, there are still some problems which include the incomplete utilization of skeleton graph features and the lacking of logical adjacency information between nodes in adjacency matrix. In this article, a human action recognition algorithm is proposed based on multiple features from the skeleton graph to solve these problems. More specifically, an improved adjacency matrix is constructed to make full use of the multiple skeleton graph features. These features include local differential features, multi‐scale edge features, features of the original skeleton graph, nodal features, and nodal motion features. Extensive results are conducted on four standard datasets (NTU RGB‐D 60, NTU RGB‐D 120, Kinetics, and Northwestern‐UCLA). The experimental results show that the proposed algorithm outperforms the SOTA action recognition algorithms.
Databáze: Directory of Open Access Journals
Externí odkaz: