Deep Learning and Attention-Based Methods for Human Activity Recognition and Anticipation: A Comprehensive Review

Abstract In recent years, there has been a significant increase in research focused on Human Activity Analysis (HAA). This field has progressed from basic activity recognition tasks to addressing more challenging ones, such as predicting future human actions based on partially observed videos and even predicting actions before they happen. The evolution of HAA has been driven by recent advancements in attention-based models like Transformers, along with a wide range of applications from security surveillance to advanced monitoring systems, behaviour analysis, and more. A comprehensive review of HAA literature from 2017 to 2025, with a novel taxonomy emphasising activity recognition, prediction, and anticipation, is presented. We critically review and examine recognition methods from trimmed and untrimmed videos, context-aware and trajectory-based prediction, and short-term and long-term anticipation. Through a comprehensive analysis, we review and evaluate key aspects of this domain, including attention-based contextual comprehension, temporal dynamics modelling, and multi-model fusion methods. Furthermore, we critically examine and assess the public datasets utilised in driving this research forward, pinpointing limitations and primary challenges within this domain. Finally, the paper provides a summary of recent developments in HAA and suggests future directions, with the hope that it will serve as a valuable reference for researchers in the field.