A Study On Vehicle Detection And Tracking In Foggy Conditions Based On Video Analysis

As an important part of intelligent transportation system(ITS),technology of vehicle detection and tracking based on video plays an important role in traffic parameters extraction,traffic supervision and guidance.At present,the vehicle detection and tracking technology is relatively mature in good illumination conditions,but the research on other special weather conditions is relatively few.Due to the complexity of road conditions and the exponentially increased risk factors of driving,vehicle detection and tracking technology in foggy weather has an important significance for traffic safety management under extreme weather conditions.In this thesis,the vehicle detection and tracking technology in foggy conditions is studied,the main work and results are as follows:(1)The method of vehicle detection in mist environment is studied,and an adaptive hybrid Gauss Mixture Model(GMM)based vehicle detection method is proposed.Firstly,to enhance the contrast of the image,a threshold segmentation method is used in the enhancement of the fog degraded image.Secondly,the background model is initializated with GMM,and the parameters of the model are updated and vehicles are detected according to the results of Gauss matching,then the background model is updated through successive frame difference method.To solve the problem of high time complexity resulted from fixed K and the relatively low accuracy caused by replacing the Gaussian distribution with least weight by the current pixel value,the K value is adaptively increased and reduced in the matching process and the background background model is established through continuous optimization of Gauss distribution matching.Finally,the minimum bounding rectangle of the vehicle is extracted by morphological processing and region growing method.Experiments show that compared with the classical GMM method,the proposed method is more acuurated with less computation.(2)The method of vehicle detection in dense fog environment is studied,and a detection method based on the vehicle headlights and fog lights paired association is proposed.The initial segmentation of the vehicle is completed through the adaptive threshold calculation.Using morphological processing and extraction of connected regions based on region growing method,the minimum bounding rectangle of vehicle is obtained.According to the characteristics of the external rectangular box and the relationship between the lights of the car lights,the relationship between the position of a vehicle headlight lamp and the fog lamp of is also in consideration,the vehicle detection is completed.In view of the influence of the reflection light on the image in foggy conditions,the reflection light is removed according to the symmetry and the characteristic of neighbourhoodt in this thesis.(3)The method of vehicle tracking in foggy conditions is studied.According to the rectangular frame extracted in the part of vehicle detection in foggy conditions,the multiple eigenvalues are calculated,then predicting the position of the vehicle in the next frame by Kalman filter,matching the characteristic value of the vehicle with the position as the center.Vehicle with the highest matching coefficient is the target vehicle.With this method,the vehicle tracking is realized.This method uses Kalman filter to predict,which helps narrow the search range,and reduces the amount of algorithm computation.