Video Surveillance Systems, Fire Detection Technology

With the deepening of urbanization, there are more and more skyscraper, subway, gymnasium, schools, shopping malls and parks emerging around people. At the same time, the losses that caused by fire is also growing in the city. The traditional fire monitoring methods have been not suited to the rapid development of the society. In recent years, with the rapid development of computer and digital image processing technology, a new safety monitoring system based on video image detection has gradually become the mainstream of research.The thesis mainly studies the flame detection technology in the video surveillance system and realizes a flame detection method which achieves real-time automatic fire detection by analyzing and processing live video images that captures from camera. Firstly, the method of moving region detection which is the basis of flame detection and directly affects the flame detection results and efficiency is discussed. According to the needs of the actual complex scene, the system uses the improved CodeBook algorithm to extract the moving regions. Compared with the methods of mixture of Gaussians, The Codebook method can detect more accurate, faster and handle more dynamic background. After moving regions detection, the suspicious moving areas are extracted.To achieve the flame detection, we must extract the characteristics which can distinguish other objects. The flame has complex change process and forms in the burning process. Its color features, flicker frequency characteristic, relative stability characteristic, space change information and irregular shape characteristic are extracted by observing. Firstly, the color model is established. If the suspicious region meets the requirement of the color model, we think that it is a fire-colored region. And then two methods: short-time zero crossing rate and temporal wavelet transform are proposed to detect flicker frequency. At the same time we use short-time contour coincidence rate to describe relative stability of the suspicious region. Spatial wavelet transform and circularity are utilized to analyze space change information and irregular shape respectively. At last, all of the characters extracted above are combined by weighted voting scheme to achieve accurate flame detection and judgments. After testing many videos in different environments, experiments show that this method can detect the flame effectively, and has better real-time and anti-interference ability. It can be widely applied in many domains of the city fire safety system.