OPT OpenIR  > 光学影像学习与分析中心
Action recognition using spatial-optical data organization and sequential learning framework
Yuan, Yuan1; Zhao, Yang1,2; Wang, Qi3,4
作者部门光学影像学习与分析中心
2018-11-13
发表期刊NEUROCOMPUTING
ISSN0925-2312;1872-8286
卷号315页码:221-233
产权排序1
摘要

Recognizing human actions in videos is a challenging problem owning to complex motion appearance, various backgrounds and semantic gap between low-level features and high-level semantics. Existing methods have scored some achievements and many new thoughts have been proposed for action recognition. They focus on designing a robust feature description and training an elaborate learning model, and many of them can benefit from a two-stream network with a stack of RGB frames and optical flow frames. However, these features for human action representation are struggling with the limited feature representation as RGB videos are confused by static appearance redundancy and optical flow videos cannot represent the detailed appearance. To solve these problems, we propose an efficient algorithm based on the spatial-optical data organization and the sequential learning framework. There are two contributions of our method: a novel data organization based on hierarchical weighting segmentation and optical flow for video representation, and a lightweight deep learning model based on the Convolutional 3D (C3D) network and the Recurrent Neural Network (RNN) for complicated action recognition. The new data organization aggregates the merits of motion appearance, movement trajectories and optical flow in a creative way to highlight the meaningful information. And the proposed lightweight model has an insight into patterns and semantics of sequential data by low-level spatiotemporal feature extraction and high-level information mining. The proposed method is evaluated on the state-of-the-art dataset and the results demonstrate that our method have a good performance for complex human action recognition. (c) 2018 Elsevier B.V. All rights reserved.

关键词Action Recognition Spatiotemporal Feature Deep Learning Sequential Learning Framework
DOI10.1016/j.neucom.2018.06.071
收录类别SCI
语种英语
WOS记录号WOS:000445934400022
出版者ELSEVIER SCIENCE BV
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30659
专题光学影像学习与分析中心
通讯作者Wang, Qi
作者单位1.Chinese Acad Sci, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing 049, Peoples R China
3.Northwestern Polytech Univ, Sch Comp Sci, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
4.Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714
Yuan, Yuan,Zhao, Yang,Wang, Qi. Action recognition using spatial-optical data organization and sequential learning framework[J]. NEUROCOMPUTING,2018,315:221-233.
APA Yuan, Yuan,Zhao, Yang,&Wang, Qi.(2018).Action recognition using spatial-optical data organization and sequential learning framework.NEUROCOMPUTING,315,221-233.
MLA Yuan, Yuan,et al."Action recognition using spatial-optical data organization and sequential learning framework".NEUROCOMPUTING 315(2018):221-233.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Action recognition u(2476KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Yuan, Yuan]的文章
[Zhao, Yang]的文章
[Wang, Qi]的文章
百度学术
百度学术中相似的文章
[Yuan, Yuan]的文章
[Zhao, Yang]的文章
[Wang, Qi]的文章
必应学术
必应学术中相似的文章
[Yuan, Yuan]的文章
[Zhao, Yang]的文章
[Wang, Qi]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Action recognition using spatial-optical data organization and sequential learning framework.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。