提出一种基于动态和静态联合特征的行人检测方法,用于运动背景下的行人检测。运动背景的检测难度在于背景与目标的分离,该方法采用一种改进的Nagel二阶梯度光流算法生成图像的光流场,从中提取行人运动特征(MBH)和IMH(internal motion histograms),增强特征重复性以提高鉴别能力。实验中使用Libsvm训练线性SVM(support vector machine)分类器,使用Mean Shift算法优化分类结果。实验在1 093组图像上获得98%的识别率,证明该方法可以在运动背景下的图像序列上获得较出色的检测效果。

In this paper,we present an approach for detecting pedestrians from moving backgrounds which is based on compound features combined with motion and static features. It is difficult to discriminate human bodies from a moving background. We improve Nagel's second-order gradient optical flow algorithm and enrich the inner repeatability of MBH (motion boundary histograms) and IMH (internal motion histograms) motion feature based on the flow. We train a linear SVM (support vector machine) classifier using features made from a pedestrian sample. A fixed window sliding over image and classify results are optimized using the mean shift algorithm. The accuracy is 98% on test of 1 093 group images,which is better than the results using other methods.