Research On Video-based Early Smoke And Flame Grading-detection Under Dynamic Scene

Video fire detection is one of the most active research topics being valuable for both theoretical and practical research in computer vision especially has a wide spectrum of promising applications in video surveillance for early fire alarms in public security. However, because of the polytropy of the fire derivatives and complexity of scene, video fire detection becomes a difficult problem with large challenges, yet there are no general theories or algorithms have formed so far. In this paper, it is mainly research the methodology of video fire detection, in order to improve the sensitivity of fire alert system and reduce false alarm, so as to promote the performance of video fire detection system.The research contents of this paper are mainly composed of 4 parts: background-rebuild and moving object extraction, static features extraction, dynamic features extraction, fire grading-detection base on BP neural network.The principle of Gaussian and some other background models are analyzed on the basis of further study on traditional moving detection algorithms and, then the two step background model which is suitable for this paper is selected to extract the initial object, combine with background subtraction and mathematical morphology, and thus remove the static interferences. After extracting the moving region the distribution in specific color space of flame and smoke are found by investigation on flame and smoke images, and the corresponding color models are build to segment the flame and smoke like regions.The motion accumulation, flicker frequency, motion orientation, motion consistency, motion degree, etc, those dynamic criterions of the segment regions are analyzed based on comparisons between flame, smoke and other disturbing objects, more over, the analysis and computation methods of the criterions are brought forward. The fire criterions fusion scheme based on BP neural network is discussed, firstly, the basic contents of artificial neural network is introduced, and then the definition of characters, the input/output unit and design scheme of BP neural network are presented, at last, the designed neural network is used for grading-detection of early smoke and flame images.25 video clips under different condition are used for training neural network and, the neural network is used for recognizing other 35 video clips, among these, only 2 video clips are missed and 1 video clips is error recognized. The results show that the system can recognize the flame and smoke in 200 frames and has good anti-interference ability.