基于颜色、运动和形状的监控摄像机烟雾检测

A. Filonenko, Danilo Cáceres Hernández, Wahyono, K. Jo
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引用次数: 6

摘要

本文提出了一种利用颜色、形状和运动特征的监控摄像机烟雾检测方法。相机是不可移动的,通过背景减法简化了检测任务。颜色分析强调运动的物体有更高的可能性是真实的烟雾。由于背景减法的性能有限,一个真实的烟雾区域被表示为许多单独的像素。这些像素使用基于密度的空间聚类应用与噪声方法和形态学操作相结合。利用边界粗糙度和面积变异性来评价候选烟的形状。不规则的烟密度可以通过边缘密度来检测。通过运动分析证实了烟雾的动态特性。对不同数据集的测试显示了该方法的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smoke detection for surveillance cameras based on color, motion, and shape
This paper presents a smoke detection approach for surveillance cameras that uses color, shape, and motion characteristics. The fact a camera is immovable simplifies detection task by applying background subtraction. Color analysis emphasizes moving objects that have higher probability to be actual smoke. Due to limited performance of background subtraction, a real smoke region is represented as many separate pixels. These pixels are combined using density-based spatial clustering of applications with noise method and morphological operations. Shape of smoke candidate is evaluated using boundary roughness and area variability. Irregular density of smoke can be checked by edge density. The dynamic nature of smoke is confirmed by motion analysis. Tests on various datasets have shown consistency of the method.
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