Human Action Anticipation: A Survey
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910656212500480 |
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| author | Lai, Bolin Toyer, Sam Nagarajan, Tushar Girdhar, Rohit Zha, Shengxin Rehg, James M. Kitani, Kris Grauman, Kristen Desai, Ruta Liu, Miao |
| author_facet | Lai, Bolin Toyer, Sam Nagarajan, Tushar Girdhar, Rohit Zha, Shengxin Rehg, James M. Kitani, Kris Grauman, Kristen Desai, Ruta Liu, Miao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human Action Anticipation: A Survey Lai, Bolin Toyer, Sam Nagarajan, Tushar Girdhar, Rohit Zha, Shengxin Rehg, James M. Kitani, Kris Grauman, Kristen Desai, Ruta Liu, Miao Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Human Action Anticipation: A Survey |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.14045 |