A Survey on Deep Learning Techniques for Action Anticipation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918439668416512 |
|---|---|
| author | Zhong, Zeyun Martin, Manuel Voit, Michael Gall, Juergen Beyerer, Jürgen |
| author_facet | Zhong, Zeyun Martin, Manuel Voit, Michael Gall, Juergen Beyerer, Jürgen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_17257 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey on Deep Learning Techniques for Action Anticipation Zhong, Zeyun Martin, Manuel Voit, Michael Gall, Juergen Beyerer, Jürgen Computer Vision and Pattern Recognition 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. |
| title | A Survey on Deep Learning Techniques for Action Anticipation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.17257 |