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Smoke Detection Method Based on LBP and SVM from Surveillance Camera

机译:基于LBP和SVM的监控摄像机烟雾探测方法

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摘要

Wildfire is a regular incident worldwide today. It destroys forests and also the living areas of wild animals. So, to reduce the harmful effects of such disasters this paper describes a method of smoke detection for surveillance cameras. This smoke detection will ease the fire detection. Proposed method is based on Local Binary Pattern (LBP) and Support Vector Machine (SVM). Initially, Approximate Median Filtering Algorithm was applied to subtract the background from input frame. Then, shape based filtering method was applied to get the region of interest. Thirdly, LBP values and histograms were calculated from the pixels of region of interest to form a feature vector. The proposed method also applied Bhattacharyya coefficients to verify the smoke region for accurate result. Finally, SVM classified the region of interest as smoke image. Results using real scene data show that the proposed method can give accurate results in different conditions of real world situations.
机译:野火是当今世界范围内的常见事件。它破坏了森林以及野生动物的生活区。因此,为了减少此类灾难的有害影响,本文介绍了一种用于监视摄像机的烟雾检测方法。这种烟雾探测将简化火灾探测。所提出的方法基于局部二进制模式(LBP)和支持向量机(SVM)。最初,应用近似中值滤波算法从输入帧中减去背景。然后,基于形状的滤波方法被应用于获得感兴趣区域。第三,从感兴趣区域的像素计算LBP值和直方图以形成特征向量。所提出的方法还应用了Bhattacharyya系数来验证烟雾区域以获得准确的结果。最后,SVM将关注区域分类为烟雾图像。使用真实场景数据的结果表明,所提出的方法可以在现实世界中不同条件下给出准确的结果。

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