Zhong, ZeyunZeyunZhongMartin, ManuelManuelMartinVoit, MichaelMichaelVoitGall, JürgenJürgenGallBeyerer, JürgenJürgenBeyerer2023-12-082023-12-082023-09-29https://publica.fraunhofer.de/handle/publica/45777710.48550/arXiv.2309.172572309.17257v1The 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.enaction anticipationactivities of daily livingvideo understandingdeep learningpaper