A Comprehensive Survey of Vision-Based Human Action Recognition Methods

• Main Authors: Hong-Bo Zhang, Yi-Xiang Zhang, Bineng Zhong, Qing Lei, Lijie Yang, Ji-Xiang Du, Duan-Sheng Chen
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Sensors
• Subjects: action detection; action feature; human action recognition; human–object interaction recognition; systematic survey
• Online Access: https://www.mdpi.com/1424-8220/19/5/1005

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 human⁻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.