为了提高行人检测系统的检测率,提出了一种基于混合高斯背景建模结合方向梯度直方图(histogram of oriented gradient, HOG)+支持向量机(support vector machine, SVM)的行人检测模型. 首先, 采用混合高斯模型进行前景分割,有效提取出运动目标区域; 然后,在行人识别部分通过缩小检测窗口尺寸来降低HOG特征维数; 另外,利用误识别区域, 对样本库的信息进行二次更新, 以优化SVM分类器; 最后,以随机视频帧为测试样本进行模型性能验证. 结果表明,在保证检测率和检测速率的情况下,该混合高斯结合HOG+SVM模型的误检率仅为4 %,说明该模型能够在复杂场景下实时准确地进行行人检测.

To improve detection rate of pedestrian detection system, a novel pedestrian detection model by combining the Gaussian mixture background model and histograms of oriented gradients (HOG) plus support vector machine (SVM) is proposed. First, foreground segmentation is done using the Gaussian mixture model (GMM) to extract moving target areas. In the recognition of pedestrians, the dimension of the HOG descriptor is reduced by resizing the size of the detecting window. In addition, an error recognition region is used to re-update the information of sample dataset to optimize the SVM classifier. Performance of this model is evaluated with the test frames randomly chosen from a video taken in a realistic scene. The results indicate that GMM and HOG+SVM can ensure accuracy and speed of detection, and limit false detection rate to 4 %. Real-time and accurate pedestrian detection in complex scenes are achieved.