3D Human Action Recognition: Through the eyes of researchers

作者：

Highlights：

• Focused coverage on 3D HAR including multi-modal, fusion strategies.

• Discussion of end-to-end pipeline including pre-processing.

• Critical review of past methods and suggested future directions.

• Novel Graph Convolutional Network(GCN) based HAR reviewed in detail.

• Real-world applications of HAR highlighted.

摘要

•Focused coverage on 3D HAR including multi-modal, fusion strategies.•Discussion of end-to-end pipeline including pre-processing.•Critical review of past methods and suggested future directions.•Novel Graph Convolutional Network(GCN) based HAR reviewed in detail.•Real-world applications of HAR highlighted.

论文关键词：Human Action Recognition,Survey,Datasets,Gap analysis,Graph convolutional network (GCN)

论文评审过程：Received 31 December 2020, Revised 7 October 2021, Accepted 15 December 2021, Available online 10 January 2022, Version of Record 14 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116424