Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning

IF 0.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xinguo Yu, Wu Song, Xiaopan Lyu, Bin He, Nan Ye
{"title":"Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning","authors":"Xinguo Yu, Wu Song, Xiaopan Lyu, Bin He, Nan Ye","doi":"10.4018/ijdcf.2020040102","DOIUrl":null,"url":null,"abstract":"This article presents an algorithm for reading both single and multiple digital video clocks by using a context-aware pixel periodicity method and a deep learning technique. Reading digital video clocks in real time is a very challenging problem. The first challenge is the clock digit localization. The existing pixel periodicity is not applicable to localizing multiple second-digit places. This article proposes a context-aware pixel periodicity method to identify the second-pixels of each clock. The second challenge is clock-digit recognition. For this task, the algorithms based a domain knowledge and deep learning technique is proposed to recognize clock digits. The proposed algorithm is better than the existing best one in two aspects. The first one is that it can read not only single digit video clock but also multiple digit video clocks. The other is that it requires a short length of a video clip. The experimental results show that the proposed algorithm can achieve 100% of accuracy in both localization and recognition for both single and multiple clocks.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"39 1","pages":"21-39"},"PeriodicalIF":0.6000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Digital Crime and Forensics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijdcf.2020040102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

This article presents an algorithm for reading both single and multiple digital video clocks by using a context-aware pixel periodicity method and a deep learning technique. Reading digital video clocks in real time is a very challenging problem. The first challenge is the clock digit localization. The existing pixel periodicity is not applicable to localizing multiple second-digit places. This article proposes a context-aware pixel periodicity method to identify the second-pixels of each clock. The second challenge is clock-digit recognition. For this task, the algorithms based a domain knowledge and deep learning technique is proposed to recognize clock digits. The proposed algorithm is better than the existing best one in two aspects. The first one is that it can read not only single digit video clock but also multiple digit video clocks. The other is that it requires a short length of a video clip. The experimental results show that the proposed algorithm can achieve 100% of accuracy in both localization and recognition for both single and multiple clocks.
使用上下文感知像素周期性和深度学习读取单个和多个数字视频时钟
本文提出了一种使用上下文感知像素周期性方法和深度学习技术读取单个和多个数字视频时钟的算法。实时读取数字视频时钟是一个非常具有挑战性的问题。第一个挑战是时钟数字定位。现有的像素周期性不适用于定位多个第二位数字位置。本文提出了一种上下文感知的像素周期性方法来识别每个时钟的第二像素。第二个挑战是时钟数字识别。为此,提出了基于领域知识和深度学习技术的时钟数字识别算法。该算法在两个方面优于现有的最佳算法。首先,它不仅可以读取个位数视频时钟,还可以读取多位数视频时钟。另一个是它需要一个很短的视频剪辑。实验结果表明,该算法对单时钟和多时钟的定位和识别准确率均达到100%。
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 求助全文
来源期刊
International Journal of Digital Crime and Forensics
International Journal of Digital Crime and Forensics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
2.70
自引率
0.00%
发文量
15
×
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