A Survey on Deep Learning Techniques for Action Anticipation
Zeyun Zhong Manuel Martin Michael Voit Juergen Gall Jürgen Beyerer
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.