The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have been introduced for action anticipation in recent years, with deep learning-based approaches being particularly popular. In this work, we review the recent advances of action anticipation algorithms with a particular focus on daily-living scenarios. Additionally, we classify these methods according to their primary contributions and summarize them in tabular form, allowing readers to grasp the details at a glance. Furthermore, we delve into the common evaluation metrics and datasets used for action anticipation and provide future directions with systematical discussions.

翻译：能够预测未来可能的人类动作对于自动驾驶和人机交互等广泛应用至关重要。因此，近年来涌现了大量用于动作预测的方法，其中基于深度学习的方法尤为流行。本文综述了动作预测算法的最新进展，特别聚焦于日常生活场景。此外，我们根据这些方法的主要贡献对其进行分类，并以表格形式进行总结，使读者能够一目了然地掌握细节。进一步地，我们深入探讨了动作预测中常用的评估指标和数据集，并系统性地讨论了未来发展方向。