基于相关向量机和方向导数的车辆识别  

Recognition of vehicle based on rvm and direction derivative function

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作  者:李占妮[1] LI Zhan-ni(School of Mechanical and Vehicle Engineering,West Anhui University,Anhui Lu’an 237012,China)

机构地区:[1]皖西学院机械与车辆工程学院,安徽六安237012

出  处:《齐齐哈尔大学学报:自然科学版》2019年第2期14-18,共5页Journal of Qiqihar University(Natural Science Edition)

基  金:皖西学院校级自然青年项目(WXZR201608);皖西学院校级产学研合作与特色培育项目(WXZR201645).

摘  要:由于图像匹配需预先组建庞大的图片库以及图像识别对灰度梯度的严重依赖性,提出利用相关向量机和灰度的方向导数对车辆进行识别。针对不同车道数将背图像作为图片库的基元,对图片进行编码;对采集到的图像进行车道线提取,确搜索域;并进行相应的网格划分,依据灰度均值确网格的属性标签,从而建立图片的特征向量,将原图与模板特征向量的2范数作为表征指标,并利用相关向量机对车辆进行分类,从而对车辆进行位;对提取的车辆区域灰度进行函数拟合,通过求解函数极值确车辆的中心区域,并建立相应的搜索方向,将满足双阈值的像素点作为最终的轮廓边缘,从而实现对车辆轮廓边缘的细化。用大量真车试验图片对模型进行测试,结果表明:所建模型仅需有限的图片库,搜素量小,识别耗费时间短,准确率高;模型对图像灰度梯度的依赖性小,且通过双阈值有效剔除伪轮廓点,细化车辆边缘。Because large picture base must be initially established for image match and image recognition is seriously dependent on gray scale gradient,this paper adopts relative vector machine and direction derivative function of gray scale to recognize vehicle.For different lane numbers,background images are taken as the elements of picture base and all imanges are encoded.The search region can be determined by extracting lane line in the collected image.In addition,the image is divided into some grids and the attribute signs of grids can be determined according to the mean of gray scale.Then characteristic vectors are established.The two norm of characteristic vectors between the initial image and the template is to be as index.And then,relative vector machine is adopted to classify vehicles and locate vehicle.The gray scale in vehicle region extracted from the initial image is fitting by functions.The centre of vehicle region can be determined by extreme value solution of function and the corresponding search direction is built.The pixels which meet double thresholds are taken as the last outline edge and the thinning edge of vehicle outline can be obtained from it.A large number of real road vehicle samples are used to test the model and the results show that the presented model has only limited picture base and search capacity is small.Furthmore,the recognition consuming time of this model is short and has higih accuracy.The model is little dependent on gray scale gradient and the fake outline pixels can be removed by double thresholds.In addition,the thinning vehicle outline edge is accomplished.

关 键 词:相关向量机 方向导数 特征向量 双阈值 细化 

分 类 号:TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]

 

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