基于多特征融合的早期火灾烟雾检测

PDF(568 KB)

大连海事大学学报 ›› 2014, Vol. 40 ›› Issue (1) : 97-100.

基于多特征融合的早期火灾烟雾检测

王琳,李爱国,王新年,虞燕风

作者信息 +

An early fire smoke detection method based on multi-features fusion

WANG Lin, LI Ai-guo, WANG Xin-nian, YU Yan-feng

Author information +

文章历史 +

摘要

提出一种基于烟雾多特征融合的图像型早期火灾烟雾检测方法.利用混合高斯模型背景差法提取前景中的疑似区域，根据早期烟雾在RGB和HIS空间的颜色模型提取疑似区域的颜色特征值，通过二维离散小波变换提取背景模糊特征值，通过计算疑似区域像素数与其最小外接矩形面积比提取轮廓不规则特征值.根据文中提出的综合判据，融合三种特征值进行烟雾判别.对公用视频和拍摄视频进行测试.结果表明，该方法能够适应室外场景变化及多种干扰条件，有效检测早期火灾烟雾.

Abstract

An early fire smoke detection method combined of color, background blurring and contour disorder features was proposed. A mixed Gaussian model of the background was established in RGB space, while the suspicious region was extracted by comparing the current frame with its background reference model. According to color model of early smoke in RGB and HIS space, the color feature of suspicious region was obtained. The background blurring feature was obtained by two-dimension discrete wavelet transformation, the disorder feature was computed by calculating ratio of pixels in suspicious region to the area of minimum bounding rectangle. The features extracted above were used to determine whether the suspicious region was smoky according to a joint judging rule. Experimental results on public test videos show that the proposed method can improve the accuracy of smoke detection and can be used in outdoor environment.

导出引用

王琳, 李爱国, 王新年, 虞燕风. 基于多特征融合的早期火灾烟雾检测[J]. 大连海事大学学报. 2014, 40(1): 97-100

WANG Lin, LI Ai-guo, WANG Xin-nian, YU Yan-feng. An early fire smoke detection method based on multi-features fusion[J]. Journal of Dalian Maritime University. 2014, 40(1): 97-100

参考文献

［1］TOREYIN B U, DEDEOGLU Y, CETIN A E. Wavelet based real-time smoke detection in video［C］//13th European Signal Processing Conference EUSIPCO. Antalya, Turkey: Curran Associates，2005: 293-296.

［2］CHEN T H, YIN Y H, HUANG S F, et al. The smoke detection for early fire-alarming system base on video processing ［C］// Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing. California, USA: IEEE Computer Society, 2006: 427-430．

［3］CALDERARA S, PICCININI P, CUCCHIARA R. Vision based smoke detection system using image energy and color information ［J］. Machine Vision and Applications, 2011,22(4): 705-719.

［4］LEE Chen-Yu, LIN Chain-Teng, HONG Chao-Ting,et al. Smoke detection using spatial and temporal analyses［J］. International Journal of Innovative Computing, Information and Control, 2012,8(7):4749-4770.

［5］GUBBI J, MARUSIC S, PALANISWAMI M. Smoke detection in video using wavelets and support vector machines ［J］. Fire Safety Journal, 2009: 44 (8): 1110-1115.

［6］STAUFFER C, GRIMSON W E L. Adaptive background mixture models for real-time tracking［C］// Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Fort Collins, Colorado, USA: IEEE Press, 1999: 246-252.

［7］YU Chun-yu, FANG Jun, WANG Jin-jun,et al. Video fire smoke detection using motion and color features［J］.

Fire Technology, 2010,46:651-663.