A Survey on Deep Learning Techniques for Action Anticipation
- URL: http://arxiv.org/abs/2309.17257v1
- Date: Fri, 29 Sep 2023 14:07:56 GMT
- Title: A Survey on Deep Learning Techniques for Action Anticipation
- Authors: Zeyun Zhong, Manuel Martin, Michael Voit, Juergen Gall, J\"urgen
Beyerer
- Abstract summary: We review the recent advances of action anticipation algorithms with a particular focus on daily-living scenarios.
We classify these methods according to their primary contributions and summarize them in tabular form.
We delve into the common evaluation metrics and datasets used for action anticipation and provide future directions with systematical discussions.
- Score: 12.336150312807561
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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