Study On Vehicle Abnormal Behavior Recognition Algorithm Under Traffic Supervising Condition

Nowadays,the rapid development of social economy makes car ownership present the linear growth.Apart from conveniences and efficiency that private cars bring people,it also generates problems such as traffic congestion and environmental pollution.Under the condition that present road resources are finite,to save as more time as possible in the travel to work or reasons alike,many vehicles tend to show some illegal abnormal behaviors which probably lead to potential risks of traffic accidents.In order to cope with this problem,in recent years,under the premise of rapid development of Intelligent Transportation System(ITS),the automatic recognition of vehicle behaviors of traffic supervising system has become a hot spot.Traffic video contains parameters such as traffic flow,vehicle behavior tracking and vehicle license plate,so it can be used to predict or to identify vehicle behaviors and traffic events.At present,the intelligent monitoring system still exists the problem of low accuracy of vehicle detection and recognition from the perspective of vehicle detection and terminal behavior identification.In order to solve the problems above,based on actual traffic video,we developed corresponding algorithms on Vehicle Detection,Trajectory Clustering,and Vehicle Abnormal Behavior Recognition model to make intelligent surveillance video identify the vehicle behavior more accurately and effectively,Referring to the achievements of predecessors and other researchers,we mainly put forward the following main technical points:(1)Based on and optimization of statistical learning method,the characteristics of vehicle PHOG with good robustness characteristics are selected.In the training part,by using Selective Search algorithm,we firstly segment the vehicle areas in the sample set as positive examples,other regions as negative examples.Then PHOG features of the positive samples and negative ones after separately being labeled 1 or 0 will be sent to the SVM to be trained for generating the initial classification model.In the test stage,test sample set is applied to evaluate the model to identify the mistakenly classified ones,which are called hard examples.They will be sent to the SVM to be implemented second-training,and the model will surely be updated.In the recognition part,the test images are firstly segmented by Selective Search algorithm to generate the candidate regions and then the PHOG features of each proposal will be extracted,which will be sent to the SVM model to be predicted as label 1 or 0 and the proposals whose features are predicted as label 1 will be reserved.(2)Because other clustering algorithm has the disadvantage of high cost of time,we proposed a new trajectory clustering algorithm after collecting the actual traffic video as the subsequent samples of recognition model.Based on FCM clustering algorithm,first of all,in the actual traffic monitoring environment,we track the target vehicles in the video to get real-time trajectory two-dimensional coordinates and all trajectories need resampling and pretreatment after written to one file.Then,least square method and fuzzy c-means method will be applied to all trajectories to realize vehicle right-lane-change,left-lane-change,left-overtaking and illegal right-overtaking track sample set classification.(3)The main method for the vehicle behavior model establishment is to extract the target trajectory characteristics of geometric parameters as the input of the model to train the classifier,but this kind of method for sequence encoding process demand higher coordinate precision and it is sensitive to the actual uncontrolled environment noise.It is difficult to get very uniform characteristic parameters,probably affecting the training process of model,so its robustness is relatively low.It is very difficult to extract the uniform parameters of the two characteristic sequences in the same lane.To maintain the overall trend of vehicle trajectory characteristics,because the convolutional neural networks keep the invariance of the height advantage translation of scaling,distortion of input samples,so in this paper,we proposed the vehicle behavior identification method which is based on the convolution neural network.To sum up,based on the actual traffic video and analyzing the characteristics of the actual vehicle traffic behavior,the new algorithms are proposed in view of the three modules respectively and also achieve relatively better recognition effect.