Research Based On Computer Vision Warning Collapsed Steel Building Fire

In recent years, computer vision is caused extensive attention in the internationalforefront of research in the field; it has developed and promoted cross-integration relateddiscipline, but also new ideas, new way to bring the signal processing. The object beingstudied computer vision, simply is studying how to get the computer through the imagesensor or other optical sensors to perceive, analyze and understand the surroundingenvironment.In this paper, fire detection and warning method collapse based on computer vision forthe research objectives, through the regional steel building features in-depth analysis ofvides images, shot segmentation method is proposed weak movement of scenarios, andbased on probabilistic graphical models multi-target vides tracking algorithm usingparticle filter to out smoke, using HOG feature pyramid algorithm characterization andidentification methods using SVM body region, exclude the interference of humanmovement.A large amount of movement information and color change information generate fromshot changes instantaneously, and only the histogram frame difference method used mayreduce the accuracy of judgment because this method can easily affected by the sunshine,and only the optical flow method used may increase the time complexity of calculation.By analyzing the characteristics of collected video data of video in weak motion, theprinciple of this method is firstly to calculate the difference between the two histograms,when the difference is greater than a certain threshold value, the optical flow method isused to calculate the motion information in video, and when the motion informationamount is great than this threshold value, it indicates that the shot cut exists.Target tracking is a very important research topic in the field of intelligent detectionand control, in many respects target tracking, particle filter, has become a major targettracking algorithm. Handle video multi-target tracking color limitations for commonparticle filter, the probabilistic graphical model introduced multi-target tracking videoanalysis, the study tracked the target is obscured target uncertainty. On the basis of dataassociation methods, probabilistic graphical models target relationship modeling andanalysis of multi-objective particle filter framework, namely the joint data associationalgorithm, occlusion of changes in uncertain multi-target video for processing, enhanceddata edge, target feature extraction. Study of this subject is returned to the scene monitoring system for fire detection screen, so we can separate the traditional firedetection equipment stripped from the steel building, either free up more space for theintegrated management of steel buildings provide meaningful reference and reference.