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Dynamic Mode Decomposition Based Video Shot Detection

Bibliographic Details
Title: Dynamic Mode Decomposition Based Video Shot Detection
Authors: Chongke Bi, Ye Yuan, JiaWan Zhang, Yun Shi, Yiqing Xiang, Yuehuan Wang, RongHui Zhang
Source: IEEE Access, Vol 6, Pp 21397-21407 (2018)
Publisher Information: IEEE, 2018.
Publication Year: 2018
Collection: LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms: Dynamic mode decomposition, video shot detection, shot boundary, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details: Shot detection is widely used in video semantic analysis, video scene segmentation, and video retrieval. However, this is still a challenging task, due to the weak boundary and a sudden change in brightness or foreground objects. In this paper, we propose a new framework based on dynamic mode decomposition (DMD) for shot boundary detection. Because the DMD can extract several temporal foreground modes and one temporal background mode from video data, shot boundaries can be detected when the amplitude changes sharply. Here, the amplitude is the DMD coefficient to restore the video. The main idea behind the shot boundaries detection is finding the amplitude change of background mode. We can reduce error detection when the illumination changes sharply or the foreground object (or camera) moves very quickly. At the same time, our algorithm has a high detection accuracy, even the color changes are not obvious, the illumination changes slowly, or the foreground objects overlap. Meanwhile, a color space for DMD is selected for reducing false detection. Finally, the effectiveness of our method will be demonstrated through detecting the shot boundaries of the various content types of videos.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2169-3536
Relation: https://ieeexplore.ieee.org/document/8334241/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2018.2825106
Access URL: https://doaj.org/article/de2f5f45939f425396d7597ce758b515
Accession Number: edsdoj.2f5f45939f425396d7597ce758b515
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  Data: Dynamic Mode Decomposition Based Video Shot Detection
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  Data: <searchLink fieldCode="AR" term="%22Chongke+Bi%22">Chongke Bi</searchLink><br /><searchLink fieldCode="AR" term="%22Ye+Yuan%22">Ye Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22JiaWan+Zhang%22">JiaWan Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Yun+Shi%22">Yun Shi</searchLink><br /><searchLink fieldCode="AR" term="%22Yiqing+Xiang%22">Yiqing Xiang</searchLink><br /><searchLink fieldCode="AR" term="%22Yuehuan+Wang%22">Yuehuan Wang</searchLink><br /><searchLink fieldCode="AR" term="%22RongHui+Zhang%22">RongHui Zhang</searchLink>
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  Data: IEEE Access, Vol 6, Pp 21397-21407 (2018)
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  Data: IEEE, 2018.
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  Data: 2018
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  Data: LCC:Electrical engineering. Electronics. Nuclear engineering
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  Data: <searchLink fieldCode="DE" term="%22Dynamic+mode+decomposition%22">Dynamic mode decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22video+shot+detection%22">video shot detection</searchLink><br /><searchLink fieldCode="DE" term="%22shot+boundary%22">shot boundary</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+engineering%2E+Electronics%2E+Nuclear+engineering%22">Electrical engineering. Electronics. Nuclear engineering</searchLink><br /><searchLink fieldCode="DE" term="%22TK1-9971%22">TK1-9971</searchLink>
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  Data: Shot detection is widely used in video semantic analysis, video scene segmentation, and video retrieval. However, this is still a challenging task, due to the weak boundary and a sudden change in brightness or foreground objects. In this paper, we propose a new framework based on dynamic mode decomposition (DMD) for shot boundary detection. Because the DMD can extract several temporal foreground modes and one temporal background mode from video data, shot boundaries can be detected when the amplitude changes sharply. Here, the amplitude is the DMD coefficient to restore the video. The main idea behind the shot boundaries detection is finding the amplitude change of background mode. We can reduce error detection when the illumination changes sharply or the foreground object (or camera) moves very quickly. At the same time, our algorithm has a high detection accuracy, even the color changes are not obvious, the illumination changes slowly, or the foreground objects overlap. Meanwhile, a color space for DMD is selected for reducing false detection. Finally, the effectiveness of our method will be demonstrated through detecting the shot boundaries of the various content types of videos.
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      – SubjectFull: video shot detection
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      – SubjectFull: shot boundary
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