A survey on deep learning techniques for action anticipation

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.

  • Published in:
    arXiv
  • Type:
    Article
  • Authors:
    Zhong, Zeyun; Martin, Manuel; Voit, Michael; Gall, Jürgen; Beyerer, Jürgen
  • Year:
    2023

Citation information

Zhong, Zeyun; Martin, Manuel; Voit, Michael; Gall, Jürgen; Beyerer, Jürgen: A survey on deep learning techniques for action anticipation, arXiv, 2023, https://arxiv.org/abs/2309.17257, Zhong.etal.2023b,

Associated Lamarr Researchers

lamarr institute person Gall Juergen - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Jürgen Gall

Principal Investigator Embodied AI to the profile