KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권11호
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  • Pages.5522-5536
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  • 2017
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Smoke detection in video sequences based on dynamic texture using volume local binary patterns

  • Lin, Gaohua (State Key Laboratory of Fire Science, University of Science and Technology of China) ;
  • Zhang, Yongming (State Key Laboratory of Fire Science, University of Science and Technology of China) ;
  • Zhang, Qixing (State Key Laboratory of Fire Science, University of Science and Technology of China) ;
  • Jia, Yang (School of Computer Science and Technology, Xi'an University of Posts and Telecommunications) ;
  • Xu, Gao (State Key Laboratory of Fire Science, University of Science and Technology of China) ;
  • Wang, Jinjun (State Key Laboratory of Fire Science, University of Science and Technology of China)
  • 투고 : 2017.03.04
  • 심사 : 2017.06.27
  • 발행 : 2017.11.30
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초록

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

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참고문헌

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피인용 문헌

  1. SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention vol.11, pp.14, 2017, https://doi.org/10.3390/rs11141702