The real-time performance of vehicle detection is very important in an intelligent transportation system. Conventional Histogram of Oriented Gradients ( HOG) method has problems of lots of dimensions of feature vector and huge calculation. Therefore , this paper studies the characteristics of gradient distribution of vehicles and the influence of feature's dimension to Support Vector Machine (SVM) 's time performance. Therefore, the paper proposes a vehicle detection method, which combines local gradient vector's mean and scatter matrix with SVM. First of all, the sampled image is divided into a number of blocks uniformly. Then the gradient vector's mean and scatter matrix are calculated as feature vectors in each block. At last, the classification and identification are performed by SVM, which further reduces the calculation by variable step size. The experimental results show that the method's accuracy is equal to conventional method, but the average recognition time is reduced to 51% of conventional method.%在物联网智能交通的车辆检测中,实时性极其重要.针对梯度方向直方图特征中特征矢量维数较多、计算量大的问题,分别对车辆梯度分布特点及支持向量机分类耗时与特征向量维数的关系进行分析,提出一种结合局部梯度矢量均值、散布矩阵特征和支持向量机进行车辆检测与提取的方法.首先,将样本图像均匀地分为若干小块；然后,分别计算块内的梯度矢量均值和散布矩阵作为样本的特征向量；最后,利用支持向量机进行分类训练与识别,其中又通过变步长法进一步减少计算量.实验结果表明,该方法的检测效果与基于梯度方向直方图特征的方法相当,但平均识别时间减少为51％.
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