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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Light Condition Estimation Based on Video Fire Detection in Spacious Buildings
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Light Condition Estimation Based on Video Fire Detection in Spacious Buildings

机译：基于宽敞建筑物视频火灾检测的光条件估计

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摘要

In industrial applications in spacious buildings, video-based fire detection system needs to endure the excessive incoming light, which makes the video overexposed. So an adaptive flame segmentation and recognition algorithm is proposed to promote the adaptability and detection rate of the video-based fire detection system for a spacious building. First, moving foreground in a video is found and luminance of the moving region is calculated to estimate the light condition. For different light conditions, different flame-color segmentation models are selected adaptively. After a series of post-processes of segmentation, the suspect flame regions are extracted for feature analysis. Then, a trained support vector machine is implemented to distinguish flame and nonflame regions. The performance of the proposed algorithm is verified on a set of videos containing flames and interference. The adaptive flame segmentation model promotes the flame segmentation resulting in different light conditions. The results are compared with those of three other methods used in the literature, revealing the proposed method to have both a better segmentation result and better precision. In flame classification, the performance of five other methods has been compared with that of the proposed SVM method, and the result shows that the SVM classifier has the best stability and the accuracy is higher than most of the other tests. The proposed method achieves average detection rate of 95.0%. The result shows that both the accuracy and robustness of segmentation have been improved and it is appropriate for industrial fire detection in spacious buildings.
机译：在宽敞的建筑中的工业应用中，基于视频的火灾探测系统需要忍受过多的入射光，这使得视频过度曝光。因此，提出了一种自适应火焰分割和识别算法，以促进基于视频的火灾探测系统的适应性和检测率为宽敞的建筑物。首先，找到视频中的前景，并且计算移动区域的亮度以估计光状况。对于不同的光线条件，自适应地选择不同的火焰彩色分段模型。在一系列分割后的分割后，提取了可疑的火焰区域以进行特征分析。然后，实现训练有素的支持向量机以区分火焰和非流域区域。在包含火焰和干扰的一组视频上验证了所提出的算法的性能。自适应火焰分割模型促进火焰分段导致不同的光线条件。结果与文献中使用的三种其他方法的结果进行了比较，揭示了所提出的方法，具有更好的分割结果和更好的精度。在火焰分类中，将五种其他方法的性能与所提出的SVM方法的性能进行了比较，结果表明，SVM分类器具有最佳稳定性，精度高于大多数其他测试。所提出的方法达到95.0％的平均检测率。结果表明，分割的准确性和稳健性都得到了改善，并且适用于宽敞的建筑物中的工业火灾检测。

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  • 来源
  • 作者

    Yang Jia; Gaohua Lin; Jinjun Wang; Jun Fang; Yongming Zhang;

  • 作者单位

    State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230027 People’s Republic of China;

    State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230027 People’s Republic of China;

    State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230027 People’s Republic of China;

    State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230027 People’s Republic of China;

    State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230027 People’s Republic of China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Flame color; Adaptive segmentation model; Motion features; Fire recognition;

    机译：火焰颜色;自适应分割模型;运动功能;消防认可;

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