摘要
以基于视频的人体动作识别为核心,首先对传统RGB动作识别领域的算法进行了全面回顾,包括传统算法和基于深度学习的算法,基于RGB视频的动作识别易受背景光照的影响识别精度不高,但有丰富的颜色外观信息;然后对RGB-D动作识别领域的算法进行分析总结,主要分为深度序列、骨骼和多特征融合三个方面,RGB-D视频具有多个模态可以为动作识别提供更多的信息,可以弥补基于RGB视频的不足但也带来了新的挑战;最后对常用数据集和未来可能的发展方向进行了展望。
Based on video action recognition,this paper firstly reviewed the traditional action recognition algorithms based on RGB,including traditional algorithms and deep learning algorithms.Because of the influence of background lighting,RGB-video-based action recognition is low accuracy,but it has rich color appearance information.Then this paper analyzed and summarized action recognition algorithms based on RGB-D,which was divided into depth sequence,bone and multi-feature fusion.RGB-D video has multiple modes and can provide more information for action recognition,which performs better than RGB video but also brings new challenges.Finally,this paper prospected the common data sets and the possible future development direction.
作者
黄晴晴
周风余
刘美珍
Huang Qingqing;Zhou Fengyu;Liu Meizhen(School of Control Science&Engineering,Shandong University,Jinan 250061,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第11期3213-3219,共7页
Application Research of Computers
基金
国家重点研发计划资助项目(2017YFB1302400)
山东省重大创新工程资助项目(2017CXGC0926)
国家面上基金资助项目(61375084)
山东省重点研发计划资助项目(2017GGX30133)。
