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The Smoke Detection Method Based on Structural Similarity of Video

机译:基于视频结构相似度的烟雾探测方法

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摘要

The structural similarity (SSIM) of image is generally used for objective quality assessment of image. The background image blurred by smoke will cause the degradation of image quality. In this paper, we discuss qualitative relation between SSIM index of smoke video and smoke density, and then propose a smoke detection method based on structural similarity of video. The experimental results demonstrate that the increasing of smoke density results in the regularly decreasing of smoke video's SSIM index. The difference of SSIM index between smoke and non-smoke distractors, such as moving people, provides an effective way to distinguish them.
机译:图像的结构相似性(SSIM)通常用于图像的客观质量评估。烟雾模糊的背景图像会导致图像质量下降。本文讨论了烟雾视频的SSIM指标与烟雾密度之间的定性关系,然后提出了一种基于视频结构相似度的烟雾检测方法。实验结果表明,烟雾密度的增加导致烟雾视频的SSIM指数有规律地降低。烟雾干扰因素和非烟雾干扰因素(例如移动的人)之间的SSIM指数差异为区分它们提供了有效的方法。

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