综述人类行为预测前沿，梳理方法、数据集与评估标准。
Human Action Anticipation: A Survey
- 系统整理动作预测领域的主流方法与技术演进
- 涵盖11个数据集的性能对比，提供可复现基准
- 适合关注人机交互、自动驾驶的研究者参考
预测未来人类行为是计算机视觉中日益热门的研究方向，广泛应用于自动驾驶、数字助手和人机交互等领域。该领域涵盖动作预测、活动预报、意图识别、目标预测等多种任务。本文综述旨在整合这一分散的研究文献，涵盖近期技术突破以及用于模型训练与评估的新大规模数据集。同时总结了各类任务的常用评估指标，并对现有方法在11个动作预测数据集上的表现进行了全面比较。本综述不仅为当前动作预测方法提供参考，也为该快速发展的研究方向指明未来路径。
原文摘要 · Abstract (English)
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.
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