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Computer Science > Computer Vision and Pattern Recognition

arXiv:2309.17257 (cs)
[Submitted on 29 Sep 2023]

Title:A Survey on Deep Learning Techniques for Action Anticipation

Authors:Zeyun Zhong, Manuel Martin, Michael Voit, Juergen Gall, Jürgen Beyerer
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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.
Comments: Submitted to TPAMI
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2309.17257 [cs.CV]
  (or arXiv:2309.17257v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2309.17257

Submission history

From: Zeyun Zhong [view email]
[v1] Fri, 29 Sep 2023 14:07:56 UTC (3,668 KB)
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