A Survey on Deep Learning Techniques for Action Anticipation
It is a survey paper, so it is incremental, summarizing existing work for researchers in computer vision and robotics.
This paper surveys deep learning techniques for action anticipation, focusing on daily-living scenarios, and provides a classification, summary, and future directions for the field.
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.