基于深度学习的视频预测研究综述

近年来，深度学习算法在众多有监督学习问题上取得了卓越的成果，其在精度、效率和智能化等方面的性能远超传统机器学习算法，部分甚至超越了人类水平。当前，深度学习研究者的研究兴趣逐渐从监督学习转移到强化学习、半监督学习以及无监督学习领域。视频预测算法，因其可以利用海量无标注自然数据去学习视频的内在表征，且在机器人决策、无人驾驶和视频理解等领域具有广泛的应用价值，近两年来得到快速发展。本文论述了视频预测算法的发展背景和深度学习的发展历史，简要介绍了人体动作、物体运动和移动轨迹的预测，重点介绍了基于深度学习的视频预测的主流方法和模型，最后总结了当前该领域存在的问题和发展前景。

Review of deep learning-based video prediction

In recent years, deep learning algorithms have made significant achievements on various supervised learning problems, with their accuracy, efficiency, and intelligence outperforming traditional machine learning algorithms, in some instances even beyond human capability. Currently, deep learning researchers are gradually turning their interests from supervised learning to the areas of reinforcement learning, weakly supervised learning, and unsupervised learning. Video prediction algorithms have developed rapidly in the last two years due to its capability of using a large amount of unlabeled and naturalistic data to construct the forthcoming video as well as its widespread application value in decision making, autonomous driving, video comprehension, and other fields. In this paper, we review the development background of the video prediction algorithms and the history of deep learning. Then, we briefly introduce the human activity, object movement, and trajectory prediction algorithms, with a focus on mainstream video prediction methods that are based on deep learning. We summarize current problems related to this research and consider the future prospects of this field.