Welcome to the InfoSci Platform
Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning

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

Xinguo Yu, Wu Song, Xiaopan Lyu, Bin He, Nan Ye
Copyright: © 2020 |Volume: 12 |Issue: 2 |Pages: 19
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799805809|DOI: 10.4018/IJDCF.2020040102
Cite Article Cite Article

MLA

Yu, Xinguo, et al. "Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning." IJDCF vol.12, no.2 2020: pp.21-39. http://doi.org/10.4018/IJDCF.2020040102

APA

Yu, X., Song, W., Lyu, X., He, B., & Ye, N. (2020). Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning. International Journal of Digital Crime and Forensics (IJDCF), 12(2), 21-39. http://doi.org/10.4018/IJDCF.2020040102

Chicago

Yu, Xinguo, et al. "Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning," International Journal of Digital Crime and Forensics (IJDCF) 12, no.2: 21-39. http://doi.org/10.4018/IJDCF.2020040102

Export Reference

Mendeley
Favorite Full-Issue Download

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.