Research On Vehicle Detection And Tracking For Front Collision Warning

In recent years,the morphological distribution of road traffic accidents in China shows that the proportion of accidents caused by front collision was the largest in road traffic accidents,and the front collision warning system of the advanced driver assistant systems can warn the driver before the collision occurs,thus avoiding the occurrence of traffic accidents.Accurate and fast vehicle detection and vehicle tracking is the key to the front collision warning system.In this paper,the vehicle detection and vehicle tracking methods is researched,which provides technical support and theoretical guidance for front collision warning system.(1)Research on candidate regional extraction methods for significant features.In this paper,in order to improve the search efficiency of vehicle detection algorithms in the whole area of the image,the significant region of the image is extracted before vehicle detection,and use it as a vehicle candidate area.According to the characteristics of the image spectrum,firstly,the one-dimensional radial average power spectrum of the image is sought;then the image of a one-dimensional radial average power spectrum of residual error is calculated,and through the Fourier inverse change,the residual image of significant figure is calculated.Base on this process,a multi-scale method for generating significant figure is put forward,The significant graph of multiple scales is superimposed to obtain the final image.Finally,the image segmentation by Meanshift algorithm is used to isolate the significant regions in the image.(2)Research on vehicle detection method for front collision warning.In order to reduce the complexity of HOG feature calculation and improve the real-time and accuracy of the algorithm,the HOG feature accordingly is improved in this paper.Firstly,the dimension of HOG feature vector dimension is reduced,and the characteristics of small influence are eliminated.And in generating the HOG feature pyramid,a rapid feature pyramid method is proposed.The scale characteristics of a certain scale feature in the characteristic pyramid are estimated to reduce the computational complexity.Then,a multi-angle model is proposed for forward vehicle detection.And improve the accuracy of the detection by using the vehicle's lights,Windows and other vehicles.Finally,using PASCAL VOC 2007 data set,KITTI data sets and vehicle video,which are collected by the vehicle recorder,verify the effectiveness of the improved algorithm.(3)Research on vehicle tracking method for front collision warning.The principle of nuclear related filtering algorithm is studied.Aiming at the shortcomings of the mesoscale invariant of the nuclear related filtering algorithm,a kind of adaptive nuclear filtering vehicle and tracking algorithm is proposed.Firstly,the central position of the target is obtained through the nuclear related filtering,and the target is divided into four equal parts.Then the response value of each image block is calculated and the scale of the next frame is updated to realize the adaptive transformation of the scale.The Visual Tracker Benchmark dataset and the vehicle video of the vehicle recorder are used to verify the effectiveness of the improved algorithm.The comprehensive test shows that the proposed vehicle detection and tracking algorithm has better robustness and detection accuracy on the corresponding data set,and has certain application value for the research and development of vehicle detection and tracking algorithm.