Research On Key Technology Of Visual Supervision System For Large-scale Construction Sites

The large-scale construction site needs to take effective management because of the heavy workloads of workers and vehicles, and the potential security risk. This thesis focuses on the implementary requirements of this management and targets the key technology of visual supervision system for the large-scale construction site.Firstly, in order to solve the disadvantage of manual administration such as the limited supervision scope, the latent negligence and the additional resources consuming and so on. A visual supervision system which is designed based on the engineering application requirements is developed for the large-scale construction site.Secondly, aiming at the problem that the existing detection algorithm of the moving target influenced by the wheel trace at the large-scale construction site entrance, an improved background subtraction algorithm based on frame accumulated change matrix is proposed by analyzing the historical change of pixel value in this thesis. The main ideals of proposed method is to update the zero value points of the accumulated change matrix in a frame into the background points according the characteristic that the historical accumulated change of wheel trace region is much weaker than the dynamic moving targets. The experiment results show that the proposed algorithm contributes significantly to decrease the disturbance of wheel trace, and the F1 indicator of the foreground detection is increased about 24.4%, 14.5% and 27.3% compared to the traditional background subtraction method, traditional inter frame difference method and background subtraction method with the inter frame difference. In addition, in order to address the partial foreground splitting problem, which is caused by the inherent defect of the background subtraction algorithm, a moving target correction algorithm based on the spatial information is developed by using the position relationship of the pseudo sub-targets, the experiment shows that can solving this problem of the foreground splitting.Thirdly, according to the supervision demand forlarge-scale construction sitesincluding the classification and statistics of the workers and vehicles in and out, this paper designedan algorithm for supervision of workers and vehicles. At first, according to the application of tracking and real-time requirements, this paper designed acost function based on inter frame target spatial relationship realizing tracking. And then according to the moving characteristics of the target, designed a target classification and statistics algorithm based on tracking and counting line, to realize the classification and counting function of the system for the worker and vehicle targets. What's more, according to the classification and safety requirements oflarge-scale construction sites, aiming at the problem that existing helmet detection algorithms using RGB model are sensitive to brightness, this paper proposed a helmet detection algorithm based on the local HSV model, which detecting the helmet by the HSV feature and contour feature from a specific region of the worker target, and the accuracy of the experiment is 92.67%, better than the existing algorithm effect by 8.67%, also more suitable for the environment in this paper. Generally, this paper took a comprehensive experiment on the supervision algorithm,and the result shows that the designed algorithm for supervision of workers and vehiclescan classify and count the targets effectively, and its accuracy up to87.18%, basically meet the supervision requirements for large-scale construction sites.Lastly, according to the phenomenonof appearing a small amount of road false targetscaused by background environment mutation in large-scale construction sites, based on the analysis of the gray histogram characteristics of the road false targets and the real targets, aiming at the problem that the existing features cannot distinguish the road pseudo targets out, this paper proposed and constructed the richness feature, and optimizing the feature calculation by removing the interference of the outside road non-front points.The comparative experiment shows that the proposed and constructed richness feature can describing the road false targets better than the existing six gray histogram features, and the comprehensive classification rate up to 97.3%, which can effectively detect the road false targets.