A Comprehensive Survey of Vision-Based Human Action Recognition Methods.
Sensors (Basel)
; 19(5)2019 Feb 27.
Article
em En
| MEDLINE
| ID: mdl-30818796
ABSTRACT
Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in humanâ»object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Visão Ocular
/
Percepção Visual
/
Reconhecimento Automatizado de Padrão
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article