Human Action Recognition Systems: A Review of the Trends and State-of-the-Art

Abstract

Human action recognition (HAR), deeply rooted in computer vision, video surveillance, automated observation, and human-computer interaction (HCI), enables precise identification of human actions. Numerous research groups have dedicated their efforts to various applications and problem domains in HAR systems. They trained classification models using diverse datasets, enhanced hardware capabilities and employed different metrics to assess performance. Although several surveys and review articles have been published periodically to highlight research advancements in HAR, there is currently no comprehensive and up-to-date study that encompasses architecture, application areas, techniques/algorithms, and evaluation methods as well as challenges and issues. To bridge this gap in the literature, this article presents a comprehensive analysis of the current state of HAR systems by thoroughly examining a meticulously chosen collection of 135 publications published within the past two decades. These findings have implications for researchers engaged in different aspects of HAR systems.

Keywords

• Human action recognition
• computer vision
• machine learning
• video surveillance
• HAR architecture