Research On Detection Method Of Foreign Object Intrusion In Railway Perimeter

With the rapid development of the railway industry,a large number of passenger dedicated lines have been put into use,which has facilitated people’s travel,but at the same time,the safety of train operation has also been paid more and more attention.Among the many factors that affect the safe operation of trains,foreign object intrusion is particularly prominent due to its sudden and high unpredictability.Traditional methods of foreign object intrusion detection can cause false and missed detection due to the influence of factors such as the wide span of railway lines and changes in the natural environment along the lines.Moreover,the failure to detect foreign object in advance in the forbidden areas makes the train drivers have insufficient response time.Therefore,the method reliability is difficult to guarantee.In view of the above deficiencies,this dissertation proposed a detection method of foreign object intrusion in railway perimeter.Firstly,the perimeter area is given constraints.Secondly,the moving object foreground is accurately segmented according to the improved ViBe algorithm based on adaptive detection of moving object,and then the segmented foreground binary image area is weighted to determine whether it is a foreign object intruding the perimeter.Finally,to obtain the real-time movement state of foreign object in the perimeter,the intruding foreign object in the perimeter area is realized by the Kalman filter tracking algorithm based on feature matching.This dissertation aims to improve the accuracy of foreign object intrusion detection in the perimeter of the railway.The main work is as follows:(1)In the foreground segmentation stage,aiming at the existence of Ghost area in the detection results of traditional ViBe algorithm,and being affected by environmental changes,it is easy to cause false detection or missed detection when extracting the foreground,an improved ViBe algorithm based on adaptive detection of moving object is proposed.Firstly,during the initialization of the background model,obtain the real background by setting the adjustment parameters for the mean background modeling,and use the background to initialize the ViBe background model.Secondly,in the process of foreground detection,an adaptive radius threshold is introduced to conduct the foreground adaptive detection according to the scene changes.Finally,mathematical morphology close operation is used tofill the holes in the detection results.The simulation results show that the proposed method can effectively suppress Ghost area and detect the foreground object in a high dynamic environment.(2)In the object tracking stage,in order to avoid the problems of false tracking and missing tracking in object tracking,a Kalman filter tracking algorithm based on feature matching is proposed.Firstly,a Kalman filter tracking model is established for the detected object.Then,the Kalman filter algorithm is used to predict the position of the object in the next frame.Finally,the matching object is determined by the matching strategy.The simulation results show that the proposed method is robust to real-time continuous tracking of single and multiple objects.(3)After the object foreground segmentation algorithm and object tracking algorithm are proposed,a set of intrusion detection method for foreign object in railway perimeter are designed.Firstly,the identification process of the perimeter area is explained in detail.Secondly,the judgment conditions for the foreign object are set.Finally,the general process of foreign object intrusion detection in railway perimeter is constructed.Using the railway video taken as a simulation experiment,the results show that compared with the traditional foreign object intrusion detection method,the detection accuracy of the proposed method is more than 95%.