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首页> 外文期刊>International journal of digital crime and forensics >Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning
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Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning

机译:使用上下文感知像素周期和深度学习读取单个和多个数字视频时钟

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摘要

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.
机译:本文提出了一种通过使用上下文感知像素周期性方法和深度学习技术来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的本地化。现有的像素周期性不适用于本地化多个第二位数的位置。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个像素。第二个挑战是时钟数字识别。为此,提出了一种基于领域知识和深度学习技术的算法来识别时钟数字。该算法在两个方面都优于现有的最佳算法。第一个是它不仅可以读取一位数字视频时钟,而且可以读取多位视频时钟。另一个是它需要视频剪辑的短长度。实验结果表明,所提出的算法在单时钟和多时钟的定位和识别方面均可达到100%的精度。

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