한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제24권8호
  • /
  • Pages.19-28
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  • 2019
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
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Online Video Synopsis via Multiple Object Detection

  • Lee, JaeWon (Dept. of Computer Engineering, Kangwon National University) ;
  • Kim, DoHyeon (Dept. of Computer Engineering, Kangwon National University) ;
  • Kim, Yoon (Dept. of Computer Engineering, Kangwon National University)
  • 투고 : 2019.07.16
  • 심사 : 2019.08.12
  • 발행 : 2019.08.30
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초록

In this paper, an online video summarization algorithm based on multiple object detection is proposed. As crime has been on the rise due to the recent rapid urbanization, the people's appetite for safety has been growing and the installation of surveillance cameras such as a closed-circuit television(CCTV) has been increasing in many cities. However, it takes a lot of time and labor to retrieve and analyze a huge amount of video data from numerous CCTVs. As a result, there is an increasing demand for intelligent video recognition systems that can automatically detect and summarize various events occurring on CCTVs. Video summarization is a method of generating synopsis video of a long time original video so that users can watch it in a short time. The proposed video summarization method can be divided into two stages. The object extraction step detects a specific object in the video and extracts a specific object desired by the user. The video summary step creates a final synopsis video based on the objects extracted in the previous object extraction step. While the existed methods do not consider the interaction between objects from the original video when generating the synopsis video, in the proposed method, new object clustering algorithm can effectively maintain interaction between objects in original video in synopsis video. This paper also proposed an online optimization method that can efficiently summarize the large number of objects appearing in long-time videos. Finally, Experimental results show that the performance of the proposed method is superior to that of the existing video synopsis algorithm.

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