Research On Key Technology Of Large Area Pavement Surveillance System Based On Vision

Through the intelligent traffic system can effectively regulate the driver behavior,enhance driver awareness of traffic safety and ease the traffic congestion problem,while reducing traffic accidents has played a significant role in promoting.Vision-based intelligent system is widely used because of its convenience,effectiveness and long service life.In this paper,the key technologies of large-scale vision-based pavement supervision system are researched based on the practical application requirements.First of all,aiming at the problem that the classical foreground extraction algorithm cannot extract the foreground correctly under the condition of illumination mutation,a texture feature based on the mean of the truncation is designed on the basis of the LBP operator according to the LBP operator’s insensitivity to illumination.Through the suppression of noise and the stability of the flat region sequence,the original LBP operator is easily disturbed by noise,the flat region sequence is unstable and the obtained information of the texture map is redundant,which provides the high resolution for the subsequent motion foreground extraction quality texture information.Then,by using the texture features obtained,a background updating model that can effectively deal with the abrupt change of illumination is proposed to achieve the effect of suppressing the influence of illumination.The foreground extraction model of fusion texture feature proposed in this paper not only can extract the foreground of moving target effectively under the condition of slow illumination change,but also can extract accurately when the illumination is abrupt,the foreground accuracy is better than the average background model and mixed Gaussian Models,which has increased by 50%.Secondly,aiming at the tracking offset in the process of moving object tracking.According to the distribution rule of the target in the initial tracking frame,this paper proposes a feature point initialization model based on multi-layer distribution,which can distribute most of the initial feature points over the target to be tracked and reduce the impact of background feature points.In addition,according to the spatial location of feature points in adjacent frames,a feature point update method based on inter-frame relationship is designed.By updating and updating the feature points by using the inter-frame angle and scale information,most valid feature points can be retained Avoid tracking down failures due to too few valid feature points.Aiming at the problem of the update of the tracking frame size,according to the variation rule of the target in the image scale,this paper proposes a new updating method of the tracking frame size based on the least squares fitting.The model can adjust the size of the tracking frame in real time to ensure the tracking frame size.According to the spatial relationship between the feature point and the center of the tracking frame,this paper designs a new estimation model of the center position of the tracking frame.By using the consistency between the feature points near the motion center and the center of the tracking frame,we can accurately predict the center of a frame tracking box to suppress the tracking frame offset.The tracking stability of the proposed algorithm remains above 85%,which is more than 30% higher than that of the TLD algorithm.Finally,according to the characteristics of the triggered scene,a common triggering method based on line structure is proposed for different modes of violation triggering in different scenarios.This triggering mode can be adapted to different road monitoring scenarios.On this basis,this paper designs a universal framework for a wide range of pavement monitoring system.By integrating the four modules of foreground extraction,rolling line triggering,target tracking and target capture,in the case of a single monitoring point,the scope of supervision can be extended to 80 ~ 120 meters.The experimental results show that this triggering method is simple,adaptable,and has a good accuracy,with an average accuracy of more than 80%.