Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

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• 作者：Zhao-Guang Liu ; Yang Yang ; Xiu-Hua Ji
• 关键词：Flame detection ; Saliency detection ; Uniform local binary pattern ; YCbCr
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：10
• 期：2
• 页码：277-284
• 全文大小：1,218 KB
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• 作者单位：Zhao-Guang Liu (1)
  Yang Yang (2)
  Xiu-Hua Ji (1)
  
  1. Shandong Provincial Key Laboratory of Digital Media Technology, School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
  2. School of Information Science and Engineering, Shandong University, Jinan, China
  
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711

文摘

Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm that is based on a saliency detection technique and on the uniform local binary pattern (ULBP). In still images and video sequences, an area that contains an open flame is always noticeable because fire is an exceptional event. Thus, to utilize the color information of flame pixels, the probability density function (pdf) of the flame pixel color can be obtained using Parzen window nonparametric estimation. This a priori pdf is then fused with the saliency detection phase as top-down information so that the flame candidate area can be extracted. To reduce the number of false alarms, the image texture of the candidate area is analyzed by ULBP, and an exponential function with two parameters is utilized to model the texture of the flame area. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while ensuring the accurate classification of positive samples. The classification performance of our proposed method is proven to be better than that of alternative algorithms.