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 (Central China Normal University, Wuhan, China), Wu Song (Central China Normal University, Wuhan, China), Xiaopan Lyu (Central China Normal University, Wuhan, China), Bin He (Central China Normal University, Wuhan, China) and Nan Ye (University of Queensland, Brisbane, Australia)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IJDCF.2020040102
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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.
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Reading digital video clocks, also called time recognition, is an application-oriented research problem because clock time is the critical information of multiple applications in video analysis, video surveillance, panorama video production, and video indexing and retrieval (Bu, Sun, Ding, Miao, & Yang, 2008; Covavisaruch & Saengpanit, 2004; Li, Wan, Yan, Yu, & Xu, 2006; Li, Xu, Wan, Yan, & Yu, 2006; Xu, Wang, Wan, Li, & Duan, 2006; Yin, Hua, & Zhang, 2002; Yu, 2012; Yu & Ding, 2015; Yu, Li, & San Lee, 2008; Yu, Li, & Leong, 2009; Yu, Cheng, Wu, & Song, 2016; Yu, Ding, Zeng, & Leong, 2015; Yu, Lyu, Xiang, & Leong, 2017). Reading digital video clocks, especially reading multiple digital video clocks of a video, is a very challenging special case of reading text from overlaid video object, because reading digital video clock has multiple extra difficulties such as multiple asynchrony clocks, low resolution, and tight processing time. In fact, reading general scene text still is an open research problem(Anthimopoulos, Gatos, & Pratikakis, 2013; Epshtein, Ofek, & Wexler, 2010; Ghanei & Faez, 2015, 2016; Jaderberg, Simonyan, Vedaldi, & Zisserman, 2016; Lee, Lee, Lee, Yuille, & Koch, 2011; Lyu, Song, & Cai, 2005; Mishra, Alahari, & Jawahar, 2012; Neumann & Matas, 2012, 2013, 2015; Pan, Hou, & Liu, 2011; Shi, Wang, Xiao, Zhang, & Gao, 2013; Shi, Wang, Xiao, Gao, & Hu, 2014; Shivakumara, Phan, & Tan, 2011; Wang, Babenko, & Belongie, 2011; Wang, Wu, Coates, & Ng, 2012; Weinman, Learned-Miller, & Hanson, 2009; Zhong, Jin, Zhang, & Feng, 2016; Zhu & Zanibbi, 2016).

The clock time plays a critical role in video semantics analysis. The time on clocks often indicates the game time or event time in sports and video surveillance (Xu et al., 2006; Zhong et al., 2016; Zhu & Zanibbi, 2016). This paper considers the common case in which digital video clocks have been superimposed on video. While current videos already can have a text channel to store the encoded clock or/and timestamp information, this paper proposed algorithm does not need to use these encoded clocks or timestamps (Bu et al., 2008; Covavisaruch & Saengpanit, 2004). Thus, the proposed algorithm has a wider application range. More importantly, it can avoid the harm from the malicious modification to the encoded timestamp stored in text channel.

A lot of sports and surveillance videos have superimposed digital video clocks or/and timestamps for various reasons — such as to show game-related time or to show the time of the recording. For example, video clocks in a soccer video indicate game time lapsed at a frame, whereas reversely-running game clocks in basketball videos indicate the remaining game time at a frame and reversely-running shot clocks indicate the longest remaining time of the current ball possession. Examples of single and multiple digital video clocks in soccer and basketball videos are shown in Figure 1. In surveillance videos, superimposing digital video clocks or timestamps into videos (Yu et al., 2016) is one method guard against malicious tampering of the encoded timestamp information stored in video text channel. Hence, there is a need for algorithms for reading the superimposed digital video clock, independently of the clock or timestamp encoded in video text channel.

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