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Reading both single and multiple digital video clocks using context-aware pixel periodicity and deep learning

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成果类型:
期刊论文
作者:
Xinguo Yu(余新国);Wu Song;Xiaopan Lyu;Bin He;Nan Ye
作者机构:
[Xinguo Yu] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
[Nan Ye] University of Queensland, Brisbane, Australia
[Wu Song; Xiaopan Lyu; Bin He] Central China Normal University, Wuhan, China
语种:
英文
关键词:
Clocks;Computer graphics;E-learning;Learning algorithms;Multimedia systems;Pixels;Context-Aware;Digit recognition;Digit-Sequence;Digital videos;Domain knowledge;Learning techniques;Real time;Video clips;Deep learning
期刊:
International Journal of Digital Crime and Forensics
ISSN:
1941-6210
年:
2020
卷:
12
期:
2
页码:
21-39
基金类别:
This study is funded by National Natural Science Foundation of China (No. 61877026), Fundamental Research Funds for the Central Universities (No. CCNU17QN005), and China Postdoctoral Science Foundation (No. 2019M652678).
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
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. ...

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