对利用光流和立体视觉实现视频监控系统的贡献

Amel Ben Mahjoub, Hamdi Bou Kamcha, Mohamed Atri
{"title":"对利用光流和立体视觉实现视频监控系统的贡献","authors":"Amel Ben Mahjoub, Hamdi Bou Kamcha, Mohamed Atri","doi":"10.1109/GSCIT.2014.6970104","DOIUrl":null,"url":null,"abstract":"In this paper we combine two techniques in order to detect, track and identify person actions in a stream video. The combination of optical flow and stereovision allows also reconstructing the 3D shape of a scene from its 2D images. We take images from two cameras and match points to determine disparity, and therefore depth. The optical flow method is used to estimate motion field in a video scene. Experimental results with synthetic and real sequences, exhibit the usefulness of our proposed approach.","PeriodicalId":270622,"journal":{"name":"2014 Global Summit on Computer & Information Technology (GSCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Contribution to the realization of a video surveillance system by optical flow and stereovision\",\"authors\":\"Amel Ben Mahjoub, Hamdi Bou Kamcha, Mohamed Atri\",\"doi\":\"10.1109/GSCIT.2014.6970104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we combine two techniques in order to detect, track and identify person actions in a stream video. The combination of optical flow and stereovision allows also reconstructing the 3D shape of a scene from its 2D images. We take images from two cameras and match points to determine disparity, and therefore depth. The optical flow method is used to estimate motion field in a video scene. Experimental results with synthetic and real sequences, exhibit the usefulness of our proposed approach.\",\"PeriodicalId\":270622,\"journal\":{\"name\":\"2014 Global Summit on Computer & Information Technology (GSCIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Global Summit on Computer & Information Technology (GSCIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSCIT.2014.6970104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Global Summit on Computer & Information Technology (GSCIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSCIT.2014.6970104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们结合了两种技术来检测、跟踪和识别流视频中的人物动作。光流和立体视觉的结合也允许从其2D图像重建场景的3D形状。我们从两个摄像机和赛点获取图像来确定视差,从而确定深度。利用光流法对视频场景中的运动场进行估计。合成序列和真实序列的实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contribution to the realization of a video surveillance system by optical flow and stereovision
In this paper we combine two techniques in order to detect, track and identify person actions in a stream video. The combination of optical flow and stereovision allows also reconstructing the 3D shape of a scene from its 2D images. We take images from two cameras and match points to determine disparity, and therefore depth. The optical flow method is used to estimate motion field in a video scene. Experimental results with synthetic and real sequences, exhibit the usefulness of our proposed approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信