[1]莫凌飞,蒋红亮,李煊鹏.基于深度学习的视频预测研究综述[J].智能系统学报,2018,13(01):85-96.[doi:10.11992/tis.201707032]
 MO Lingfei,JIANG Hongliang,LI Xuanpeng.Review of deep learning-based video prediction[J].CAAI Transactions on Intelligent Systems,2018,13(01):85-96.[doi:10.11992/tis.201707032]
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基于深度学习的视频预测研究综述(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年01期
页码:
85-96
栏目:
出版日期:
2018-01-24

文章信息/Info

Title:
Review of deep learning-based video prediction
作者:
莫凌飞 蒋红亮 李煊鹏
东南大学 仪器科学与工程学院, 江苏 南京 210096
Author(s):
MO Lingfei JIANG Hongliang LI Xuanpeng
College of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
关键词:
视频预测深度学习无监督学习运动预测动作识别卷积神经网络递归神经网络自编码器
Keywords:
video predictiondeep learningunsupervised learningmotion predictionaction recognitionconvolution neural networkrecurrent neural networkauto encoder
分类号:
TP391
DOI:
10.11992/tis.201707032
摘要:
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。
Abstract:
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.

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备注/Memo

备注/Memo:
收稿日期:2017-07-19。
基金项目:国家十二五科技支撑计划重点项目(2015BAG09B01).
作者简介:莫凌飞,男,1981年生,副教授,博士,主要研究方向为机器学习与人工智能、物联网与边缘计算、智能机器人。发表学术论文多篇,其中被SCI、EI检索40余篇;蒋红亮,男,1993年生,硕士研究生,主要研究方向为深度无监督学习和计算机视觉;李煊鹏,男,1985年生,讲师,博士,主要研究方向为机器视觉、驾驶辅助系统、环境感知与信息融合。
通讯作者:莫凌飞.E-mail:lfmo@seu.edu.cn.
更新日期/Last Update: 2018-02-01