摘要
视频烟雾检测具有响应速度快、非接触等优点。但现有的视频检测方法误报率比较高。通过分析早期火灾烟雾运动规律,提出了一种适用于普通视频的烟雾检测方法。为了加快检测速度,将视频图像分割成大小相等的块,并估计每个块的运动方向。采用滑动时间窗口生成块运动方向时间序列,在此时间序列的基础上计算块的累积量和主运动方向。累积量可以反映出运动持续的程度,而主运动方向表明每个块最可能的运动方向,可以有效地抑制噪声的干扰。根据累积量和主运动方向提取出3维特征矢量,采用贝叶斯分类器进行烟雾的检测。实验结果表明,该方法鲁棒性高、速度快,能够准确地检测烟雾的出现。
Video smoke detection has many advantages over traditional methods, such as fast response, non-contact. But most of current methods for video smoke detection have high rates of false alarms. Through analyzing the characteristics of smoke motion,a novel video smoke detection is presented. In order to accelerate detection speed,video images are divided into blocks. Each block motion orientation is estimated by block matching methods. And a time sequence of motion orientation for each block is generated over a sliding time window. Then accumulation and main motion orientation are computed according to the sequence. The accumulation represents the degree of motion duration and the main motion orientation describes the maximum possible orientation of each block over the time window. A 3D feature is extracted from the accumulation and main motion orientation,and a Bayesian classifier is used for smoke detection. Experiments show that the algorithm is robust and significant for improving the accuracy of smoke detection.
出处
《中国图象图形学报》
CSCD
北大核心
2008年第4期808-813,共6页
Journal of Image and Graphics
基金
中国博士后基金项目(20070410792)
火灾科学国家重点实验室开放基金项目(HZ2006-KF03)
国家科技支撑计划(2006BAK06B07)
江西省教育厅科技项目(2007[272])
关键词
视频烟雾检测
累积量
主运动方向
特征分析
计算机视觉
video smoke detection, accumulation, main motion orlentation, feature analysis, computer vision
作者简介
袁非牛（1976-），男。副教授，博士后。主要研究方向为基于计算机视觉的火灾探测、3D可视化。E-mail：yfn@ustc.edu．cn.
