Predicting future human behavior is an increasingly popular topic in computer vision, driven by the interest in applications such as autonomous vehicles, digital assistants and human-robot interactions. The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation. We also summarize the widely-used metrics for different tasks and provide a comprehensive performance comparison of existing approaches on eleven action anticipation datasets. This survey serves as not only a reference for contemporary methodologies in action anticipation, but also a guideline for future research direction of this evolving landscape.
@article{arxiv.2410.14045,
title = {Human Action Anticipation: A Survey},
author = {Bolin Lai and Sam Toyer and Tushar Nagarajan and Rohit Girdhar and Shengxin Zha and James M. Rehg and Kris Kitani and Kristen Grauman and Ruta Desai and Miao Liu},
journal= {arXiv preprint arXiv:2410.14045},
year = {2024}
}