视频中火焰和烟气探测方法的研究
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摘要
火的使用是人类社会的一个伟大创举,它为社会发展和进步起着不可估量的作用。人们只要掌握各种用火场所的规律,提高用火的警惕性,采取可靠地预防手段,火就会造福于人类。相反,火一旦在时间或空间上失去控制,就会酿成火灾。它常常危及人的生命,破坏自然环境,损毁物质财富,给人类社会造成巨大的损失。火灾是各类灾害中发生最频繁、并极具毁灭性的灾害之一。
人类经过多年与火灾的顽强斗争,逐渐了解和掌握了一些火灾的形成和发展规律,积累许多预防火灾的经验和治理火灾的方法和措施。随着社会的发展和技术进步,人们使用各种方法和技术探测火灾。传统的火灾探测方法是基于感烟和感温的探测器,探测值超过设定阈值,则探测器报警。这是应用较为广泛和成熟的火灾探测技术。但是它们不适宜高大空间建筑和开阔区域。目前,也存在一些基于红外线和紫外线的探测技术,但此类光学探测设备相对较贵。随着视频监控系统在城市和消防重点保护单位地普及,随着人工智能和模式识别技术不断地发展,基于视频的火灾探测方法愈来愈受到重视。相比光学探测设备,CCD或CMOS等视频探测设备价格相对较低。该方法也可和已有的视频监控设备融合在一起,甚至无需购买部分硬件设备,降低了火灾探测系统的成本。它也适宜在高大空间建筑和开阔区域探测火灾。
根据早期烟气发生时的运动特性,本文提出一种摇摆运动目标检测算法；根据烟气的摇摆和扩散特征,提出一种基于摇摆和扩散特征的烟气探测算法。根据火焰的动态、颜色和面积变化特性,提出一种火焰探测综合算法。研究烟气和火焰边缘曲线的独特形状有助于火灾的探测,受此启发,提出一种基于成长型神经网络的以线段为基元曲线重建算法,尝试在下一步的工作中重建火焰和烟气的边缘曲线。
主要研究成果有以下几个方面：
1.提出一种摇摆运动目标检测算法。一些运动目标(如早期的烟气、火焰等)存在比较独特的视觉特征。它们的底部位置变化比较小。而顶部位置变化比较大。本文称之为摇摆特征。基于此特征,本文提出一种摇摆运动目标检测算法。算法首先利用模糊积分提取视频序列帧中的运动目标；其次,使用基于质心的摇摆目标识别算法把摇摆目标从运动目标中提取出来。
2.提出视频中的早期烟气具有摇摆特征。根据气体的物理运动特征,烟气流受到空气浮力和涡流的影响而扩散。一般情况下,视频中的早期烟气不断向其上方、左上方和右上方扩散。烟气底部区域位置比顶部区域位置变化小,顶部稳定性小于底部的稳定性,本文把烟气的这种特性称之为烟气的摇摆特征。
3.提出一种基于烟气摇摆特征和扩散特征的烟气识别方法。当早期火灾烟气发生时,视频中的烟气存在比较独特的视觉特征：摇摆和扩散。烟气底部位置变化相对较小,底部质心位置变化较小；而烟气顶部位置变化相对比较大,顶部质心位置变化较大。该特性表现为摇摆特征。烟气具有不断扩散的特征,这使得烟气底部浓度较高,底部基本表现为烟气的颜色；烟气顶部浓度较低,表现为背景颜色和烟气颜色的混合颜色。烟气的底部和顶部形成不同的颜色纹理：底部纹理较为粗糙,顶部纹理较细。基于烟气这两个特征,本文提出一种基于摇摆特征和扩散特征的烟气识别方法。首先利用模糊积分融合YCbCr颜色模型中各分量提取视频序列帧中运动目标；其次计算各序列帧中运动目标质心,把序列帧中具有摇摆特征的运动目标作为烟气候选区域。最后根据烟气扩散造成烟气顶部和底部形成不同的纹理,利用烟气候选区域的顶部和底部灰度共生矩阵把烟气从其他候选区域中区别出来。
4.提出一种火焰探测综合算法。首先利用模糊积分融合火焰颜色特征和纹理特征提取图像中的运动目标。算法融合了YCbCr颜色模型中相互独立的亮度分量、红色分量和局部二元模式纹理特征提取运动目标,有效地避免了视频序列帧中亮度变化对运动目标检测的干扰；其次，使用均值滤波平滑序列帧中像素RGB值,通过火焰颜色识别算法提取候选火焰区域。最后利用火焰面积变化识别算法从候选火焰区域中提取真实火焰。
5.烟气和火焰的边缘形状独特,提取和分析火焰和烟气的边缘曲线有助于火灾的探测。受此启发,本文提出以线段为基元,使用成长型神经网络重建曲线的方法。首先设定曲线的散乱点数据和一初始网络(折线),通过此算法优化网络上的神经元(顶点)位置,使网络更好地逼近散乱点数据；不断分裂网络上活动性强的神经元和删除活动性最弱的神经元,使网络上神经元的分布与散乱点数据的概率分布保持一致。此算法为下一步重建火焰和烟气的边缘曲线奠定了基础。
The use of fire is a great creation of human society, and it plays an immeasurable role for social development and progress. The fire benefits humankind if people can grasp the laws in various fire places, improve the vigilance of using fire and take a reliable means of prevention. On the contrary, once the fire flame is out of control in time or space, it will lead to a fire which often endangers human life, destroys natural environment, damages material wealth and causes huge losses to human society. The fire is the most frequent and the highly destructive one in various types of disasters.
Struggling tenaciously with the fire for many years, people got to know some laws of fire formation and development, and they also accumulated a lot of experience, methods and measures of prevention of fire. With social development and technological progress, people use a variety of fire detection methods and techniques to detect fire. Traditional fire detection methods are based on temperature detectors and smoke detectors. When the detection value exceeds the set threshold, the detector will alarm. This fire detection method is the most widely used and the most mature technology. However, they are not suitable to detect fire for large space buildings and open areas. Currently, there exist some detection technologies based on infrared and ultraviolet. However, these kinds of devices are relatively expensive. Video surveillance systems are popularized in the city and fire key protection units now. With the development of artificial intelligence and pattern recognition technique, fire detection method based on video is attached more and more importance. Comparing to the optical detection equipment, the price of CCD and CMOS is lower. This method can also use the existing video surveillance equipment and even some hardware equipment is not necessarily purchased, which can reduce the cost of fire detection system. It is also suitable to detect fire for large space buildings and open areas.
According to the motion characteristics of early smoke, a swaying object detection algorithm was presented in this paper. According to the swaying and diffusion feature of smoke, a method of early smoke detection in video using swaying and diffusion feature was presented in this paper. According to the motion, pixel color and area variation feature of fire flame, a flame detection synthesis algorithm was presented in this paper. Research on unique edge shape of smoke and flame contributes to fire detection. Inspired by this idea, a new algorithm based on growing cell structures to realize curve reconstruction using line segment was presented. Flame and smoke edge curves will be reconstructed in the next step. The main research results are as follows:
1. A swaying object detection algorithm was presented. Some moving objects such as early smoke and flame have unique vision features. Their bottom region is less mobile than their top region. This motion mode is called swaying feature in this paper. For this swaying feature, a swaying object detection algorithm was presented in this paper. Firstly, fuzzy integral is adopted to extract moving objects from video frames. Secondly, a swaying identification algorithm based on centroid calculation is used to distinguish the swaying object from other moving objects.
2. Early smoke in video has swaying feature. According to the physical motion characteristics of gas, smoke is diffused as it is influenced by buoyancy of the air and eddy. Generally speaking, early smoke in video spreads towards upward, left upper side or right upper side. Smoke bottom region is less mobile than top region and smoke upper position is less stable than the lower position. This motion feature of smoke is called swaying feature in this paper.
3. A method of early smoke detection in video using swaying and diffusion feature was presented. When an early smoke event occurs, smoke in video has unique vision features, namely swaying and diffusion. Smoke bottom position has relatively a small change, correspondingly to a relatively small change for centroid position of smoke bottom. Smoke top position has relatively a big change, correspondingly to a relatively big change for centroid position of smoke top. Therefore, a swaying feature is shown. Smoke has a continuous diffusion feature. In the bottom of smoke, smoke concentration is higher and the color basically shows smoke color. In the top of smoke, smoke concentration is lower and the color appears to be blended with smoke color and background color. In other words, the texture is comparatively rough in smoke bottom region and the texture is comparatively silky in the top region. Based on the two features of smoke, a method of early smoke detection in video using swaying and diffusion feature was presented in this paper. Firstly, choquet fuzzy integral is adopted using each component in YCbCr color model to extract moving regions from video frames, and then, a swaying identification algorithm based on centroid calculation is used to distinguish candidate smoke region from other moving regions. Secondly, smoke diffusion makes different textures between bottom region and top region of smoke. Gray Level Co-occurrence Matrixs are used to differentiate smoke from other candidate smoke regions.
4. A flame detection synthesis algorithm was presented. Firstly, choquet fuzzy integral is adopted to integrate flame color features and texture feature for extracting moving regions from video frames. In YCbCr model, red and brightness components are independent with each other. The algorithm integrates the two components and local binary pattern texture for extracting moving object, which effectively avoids the interference of brightness variation in the video frames. Secondly, mean filtering is used to smooth RGB value of video frame pixels and detected moving regions are filtered by a flame color filtering algorithm to extract candidate flame regions. Finally, a flame area variation identification algorithm is used to distinguish true flames from candidate flame regions.
5The edges of smoke and flame have unique shape. Extracting and analyzing edge curve feature of flame and smoke contribute to fire detection. An algorithm based on growing cell structures to realize curve reconstruction using line segment was presented. Given a set of unorganized data points and an initial polygonal line,the vertex position of polygonal line can be optimized by using the algorithm to make the vertexes of polygonal line gradually approach the given unorganized data points. In order to make the vertexes of polygonal line distribution coincide the space distribution of unorganized data points, the very active vertexes are split and the least active ones are deleted continually. This algorithm lays a foundation for edge curve reconstruction of flame and smoke for the next step.
人类经过多年与火灾的顽强斗争,逐渐了解和掌握了一些火灾的形成和发展规律,积累许多预防火灾的经验和治理火灾的方法和措施。随着社会的发展和技术进步,人们使用各种方法和技术探测火灾。传统的火灾探测方法是基于感烟和感温的探测器,探测值超过设定阈值,则探测器报警。这是应用较为广泛和成熟的火灾探测技术。但是它们不适宜高大空间建筑和开阔区域。目前,也存在一些基于红外线和紫外线的探测技术,但此类光学探测设备相对较贵。随着视频监控系统在城市和消防重点保护单位地普及,随着人工智能和模式识别技术不断地发展,基于视频的火灾探测方法愈来愈受到重视。相比光学探测设备,CCD或CMOS等视频探测设备价格相对较低。该方法也可和已有的视频监控设备融合在一起,甚至无需购买部分硬件设备,降低了火灾探测系统的成本。它也适宜在高大空间建筑和开阔区域探测火灾。
根据早期烟气发生时的运动特性,本文提出一种摇摆运动目标检测算法；根据烟气的摇摆和扩散特征,提出一种基于摇摆和扩散特征的烟气探测算法。根据火焰的动态、颜色和面积变化特性,提出一种火焰探测综合算法。研究烟气和火焰边缘曲线的独特形状有助于火灾的探测,受此启发,提出一种基于成长型神经网络的以线段为基元曲线重建算法,尝试在下一步的工作中重建火焰和烟气的边缘曲线。
主要研究成果有以下几个方面：
1.提出一种摇摆运动目标检测算法。一些运动目标(如早期的烟气、火焰等)存在比较独特的视觉特征。它们的底部位置变化比较小。而顶部位置变化比较大。本文称之为摇摆特征。基于此特征,本文提出一种摇摆运动目标检测算法。算法首先利用模糊积分提取视频序列帧中的运动目标；其次,使用基于质心的摇摆目标识别算法把摇摆目标从运动目标中提取出来。
2.提出视频中的早期烟气具有摇摆特征。根据气体的物理运动特征,烟气流受到空气浮力和涡流的影响而扩散。一般情况下,视频中的早期烟气不断向其上方、左上方和右上方扩散。烟气底部区域位置比顶部区域位置变化小,顶部稳定性小于底部的稳定性,本文把烟气的这种特性称之为烟气的摇摆特征。
3.提出一种基于烟气摇摆特征和扩散特征的烟气识别方法。当早期火灾烟气发生时,视频中的烟气存在比较独特的视觉特征：摇摆和扩散。烟气底部位置变化相对较小,底部质心位置变化较小；而烟气顶部位置变化相对比较大,顶部质心位置变化较大。该特性表现为摇摆特征。烟气具有不断扩散的特征,这使得烟气底部浓度较高,底部基本表现为烟气的颜色；烟气顶部浓度较低,表现为背景颜色和烟气颜色的混合颜色。烟气的底部和顶部形成不同的颜色纹理：底部纹理较为粗糙,顶部纹理较细。基于烟气这两个特征,本文提出一种基于摇摆特征和扩散特征的烟气识别方法。首先利用模糊积分融合YCbCr颜色模型中各分量提取视频序列帧中运动目标；其次计算各序列帧中运动目标质心,把序列帧中具有摇摆特征的运动目标作为烟气候选区域。最后根据烟气扩散造成烟气顶部和底部形成不同的纹理,利用烟气候选区域的顶部和底部灰度共生矩阵把烟气从其他候选区域中区别出来。
4.提出一种火焰探测综合算法。首先利用模糊积分融合火焰颜色特征和纹理特征提取图像中的运动目标。算法融合了YCbCr颜色模型中相互独立的亮度分量、红色分量和局部二元模式纹理特征提取运动目标,有效地避免了视频序列帧中亮度变化对运动目标检测的干扰；其次，使用均值滤波平滑序列帧中像素RGB值,通过火焰颜色识别算法提取候选火焰区域。最后利用火焰面积变化识别算法从候选火焰区域中提取真实火焰。
5.烟气和火焰的边缘形状独特,提取和分析火焰和烟气的边缘曲线有助于火灾的探测。受此启发,本文提出以线段为基元,使用成长型神经网络重建曲线的方法。首先设定曲线的散乱点数据和一初始网络(折线),通过此算法优化网络上的神经元(顶点)位置,使网络更好地逼近散乱点数据；不断分裂网络上活动性强的神经元和删除活动性最弱的神经元,使网络上神经元的分布与散乱点数据的概率分布保持一致。此算法为下一步重建火焰和烟气的边缘曲线奠定了基础。
The use of fire is a great creation of human society, and it plays an immeasurable role for social development and progress. The fire benefits humankind if people can grasp the laws in various fire places, improve the vigilance of using fire and take a reliable means of prevention. On the contrary, once the fire flame is out of control in time or space, it will lead to a fire which often endangers human life, destroys natural environment, damages material wealth and causes huge losses to human society. The fire is the most frequent and the highly destructive one in various types of disasters.
Struggling tenaciously with the fire for many years, people got to know some laws of fire formation and development, and they also accumulated a lot of experience, methods and measures of prevention of fire. With social development and technological progress, people use a variety of fire detection methods and techniques to detect fire. Traditional fire detection methods are based on temperature detectors and smoke detectors. When the detection value exceeds the set threshold, the detector will alarm. This fire detection method is the most widely used and the most mature technology. However, they are not suitable to detect fire for large space buildings and open areas. Currently, there exist some detection technologies based on infrared and ultraviolet. However, these kinds of devices are relatively expensive. Video surveillance systems are popularized in the city and fire key protection units now. With the development of artificial intelligence and pattern recognition technique, fire detection method based on video is attached more and more importance. Comparing to the optical detection equipment, the price of CCD and CMOS is lower. This method can also use the existing video surveillance equipment and even some hardware equipment is not necessarily purchased, which can reduce the cost of fire detection system. It is also suitable to detect fire for large space buildings and open areas.
According to the motion characteristics of early smoke, a swaying object detection algorithm was presented in this paper. According to the swaying and diffusion feature of smoke, a method of early smoke detection in video using swaying and diffusion feature was presented in this paper. According to the motion, pixel color and area variation feature of fire flame, a flame detection synthesis algorithm was presented in this paper. Research on unique edge shape of smoke and flame contributes to fire detection. Inspired by this idea, a new algorithm based on growing cell structures to realize curve reconstruction using line segment was presented. Flame and smoke edge curves will be reconstructed in the next step. The main research results are as follows:
1. A swaying object detection algorithm was presented. Some moving objects such as early smoke and flame have unique vision features. Their bottom region is less mobile than their top region. This motion mode is called swaying feature in this paper. For this swaying feature, a swaying object detection algorithm was presented in this paper. Firstly, fuzzy integral is adopted to extract moving objects from video frames. Secondly, a swaying identification algorithm based on centroid calculation is used to distinguish the swaying object from other moving objects.
2. Early smoke in video has swaying feature. According to the physical motion characteristics of gas, smoke is diffused as it is influenced by buoyancy of the air and eddy. Generally speaking, early smoke in video spreads towards upward, left upper side or right upper side. Smoke bottom region is less mobile than top region and smoke upper position is less stable than the lower position. This motion feature of smoke is called swaying feature in this paper.
3. A method of early smoke detection in video using swaying and diffusion feature was presented. When an early smoke event occurs, smoke in video has unique vision features, namely swaying and diffusion. Smoke bottom position has relatively a small change, correspondingly to a relatively small change for centroid position of smoke bottom. Smoke top position has relatively a big change, correspondingly to a relatively big change for centroid position of smoke top. Therefore, a swaying feature is shown. Smoke has a continuous diffusion feature. In the bottom of smoke, smoke concentration is higher and the color basically shows smoke color. In the top of smoke, smoke concentration is lower and the color appears to be blended with smoke color and background color. In other words, the texture is comparatively rough in smoke bottom region and the texture is comparatively silky in the top region. Based on the two features of smoke, a method of early smoke detection in video using swaying and diffusion feature was presented in this paper. Firstly, choquet fuzzy integral is adopted using each component in YCbCr color model to extract moving regions from video frames, and then, a swaying identification algorithm based on centroid calculation is used to distinguish candidate smoke region from other moving regions. Secondly, smoke diffusion makes different textures between bottom region and top region of smoke. Gray Level Co-occurrence Matrixs are used to differentiate smoke from other candidate smoke regions.
4. A flame detection synthesis algorithm was presented. Firstly, choquet fuzzy integral is adopted to integrate flame color features and texture feature for extracting moving regions from video frames. In YCbCr model, red and brightness components are independent with each other. The algorithm integrates the two components and local binary pattern texture for extracting moving object, which effectively avoids the interference of brightness variation in the video frames. Secondly, mean filtering is used to smooth RGB value of video frame pixels and detected moving regions are filtered by a flame color filtering algorithm to extract candidate flame regions. Finally, a flame area variation identification algorithm is used to distinguish true flames from candidate flame regions.
5The edges of smoke and flame have unique shape. Extracting and analyzing edge curve feature of flame and smoke contribute to fire detection. An algorithm based on growing cell structures to realize curve reconstruction using line segment was presented. Given a set of unorganized data points and an initial polygonal line,the vertex position of polygonal line can be optimized by using the algorithm to make the vertexes of polygonal line gradually approach the given unorganized data points. In order to make the vertexes of polygonal line distribution coincide the space distribution of unorganized data points, the very active vertexes are split and the least active ones are deleted continually. This algorithm lays a foundation for edge curve reconstruction of flame and smoke for the next step.
引文
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