Application of using fuzzy-neural network on PTZ camera

Cheng-Liang Lai, Sung-Ting Tsai
{"title":"Application of using fuzzy-neural network on PTZ camera","authors":"Cheng-Liang Lai, Sung-Ting Tsai","doi":"10.1109/ICMLC.2010.5580868","DOIUrl":null,"url":null,"abstract":"Surveillance system is widely applied in various fields in recent years. The demand for monitoring quality is not only to capture the object image, but also to present sharp and recognizable image. Thus, PTZ camera, which has pan and zoom functions, has become the best equipment. How to zoom in object and move camera lens so that the object is placed at the center of image becomes the major issue. This paper proposes the method of using the widest camera viewing angle as the moving range, and employing the spatial adjustment method of camera to establish the relative position of the camera and space. However, once the targeted object is unclear, the zoom in function of the PTZ camera is used; if the targeted object is not within the center of the image, the targeted object will move out of the image frame during the zoom-in process. Hence, by using the fuzzy nerves to simulate nonlinear horizontal movement and vertical rotation of PTZ camera, the image can be moved to the center of the image. In this paper, PTZ camera can zoom in image of the targeted object and move camera lens so that the object is at the frame center. Without additional adjustment, the camera lens can be moved exactly to the target position.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2010.5580868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Surveillance system is widely applied in various fields in recent years. The demand for monitoring quality is not only to capture the object image, but also to present sharp and recognizable image. Thus, PTZ camera, which has pan and zoom functions, has become the best equipment. How to zoom in object and move camera lens so that the object is placed at the center of image becomes the major issue. This paper proposes the method of using the widest camera viewing angle as the moving range, and employing the spatial adjustment method of camera to establish the relative position of the camera and space. However, once the targeted object is unclear, the zoom in function of the PTZ camera is used; if the targeted object is not within the center of the image, the targeted object will move out of the image frame during the zoom-in process. Hence, by using the fuzzy nerves to simulate nonlinear horizontal movement and vertical rotation of PTZ camera, the image can be moved to the center of the image. In this paper, PTZ camera can zoom in image of the targeted object and move camera lens so that the object is at the frame center. Without additional adjustment, the camera lens can be moved exactly to the target position.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
模糊神经网络在PTZ摄像机中的应用
近年来,监控系统被广泛应用于各个领域。对监控质量的要求不仅是捕获目标图像,而且要呈现出清晰、可识别的图像。因此,具有平移和变焦功能的PTZ相机成为了最好的设备。如何对物体进行放大和移动镜头,使物体处于图像的中心位置成为主要问题。本文提出了以最宽的摄像机视角作为移动范围的方法,并采用摄像机的空间调整方法来建立摄像机与空间的相对位置。然而,一旦目标物体不清楚,就会使用PTZ相机的放大功能;如果目标对象不在图像中心,则在放大过程中目标对象将移出图像帧。因此,利用模糊神经模拟PTZ摄像机的非线性水平运动和垂直旋转,可以将图像移动到图像的中心。在本文中,PTZ相机可以将目标物体的图像放大,并移动镜头使目标物体位于帧中心。无需额外调整,相机镜头可以精确地移动到目标位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Does joint decoding really outperform cascade processing in English-to-Chinese transliteration generation? The role of syllabification The design of energy-saving filtering mechanism for sensor networks Feature-based approach combined with hierarchical classifying strategy to relation extraction The comparative study of different Bayesian classifier models New inverse halftoning using texture-and lookup table-based learning approach
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1