SIA OpenIR  > 其他
Alternative TitleAction recognition based on improved long-term recurrent convolution network
王学微1,2; 徐方1,3; 贾凯1,3
Source Publication计算机工程与设计
Contribution Rank1
Funding Organization国家科技支撑计划基金项目(2015BAF13B01)
Keyword行为识别 卷积神经网络 递归神经网络 深度学习 模式识别
Other AbstractTo fully extract the spatial feature and time domain feature of human activity in video sequences and improve the accuracy of human action recognition algorithm, an end-to-end network combining with deep convolution neural network and recurrent neural network was presented. The stacked RGB images and the stacked optical flow images were respectively used as the network input, and the features based on the RGB images and the features based on the optical flow images were weightedly integrated as the ultimate human activity features. Experimental results show that the proposed algorithm can effectively improve the accuracy of action recognition, and obtain the average accuracy rate of 84.68%in the open dataset UCF101,which is higher than that of the long recurrent convolution network(82.34%).
Document Type期刊论文
Corresponding Author王学微
Recommended Citation
GB/T 7714
王学微,徐方,贾凯. 基于改进长效递归卷积网络的行为识别算法[J]. 计算机工程与设计,2018,39(7):2054-2058.
APA 王学微,徐方,&贾凯.(2018).基于改进长效递归卷积网络的行为识别算法.计算机工程与设计,39(7),2054-2058.
MLA 王学微,et al."基于改进长效递归卷积网络的行为识别算法".计算机工程与设计 39.7(2018):2054-2058.
Files in This Item: Download All
File Name/Size DocType Version Access License
基于改进长效递归卷积网络的行为识别算法.(337KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[王学微]'s Articles
[徐方]'s Articles
[贾凯]'s Articles
Baidu academic
Similar articles in Baidu academic
[王学微]'s Articles
[徐方]'s Articles
[贾凯]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[王学微]'s Articles
[徐方]'s Articles
[贾凯]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 基于改进长效递归卷积网络的行为识别算法.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.