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September 29, 2023
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title
A Survey on Deep Learning Techniques for Action Anticipation
Title Supplement
Published on arXiv
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.
Author(s)