提出一种多尺度方向(multi-scale orientation,简称 MSO)特征描述子用于静态图片中的人体目标检

测.MSO 特征由随机采样的图像方块组成,包含了粗特征集合与精特征集合.其中,粗特征是图像块的方向,而精特征

由 Gabor 小波幅值响应竞争获得.对于两种特征,分别采用贪心算法进行选择,并使用级联 Adaboost 算法及 SVM 训

练检测模型.基于粗特征的 Adaboost 分类器能够保证高的检测速度,而基于精特征的 SVM 分类器则保证了检测精

度.另外,通过 MSO 特征块的平移,使得所提算法能够检测多视角的人体.通过对于 MSO 特征块的装配,使得算法能

够检测人群中相互遮挡的人体目标.在INRIA公共测试集合及SDL多视角测试集合上的实验结果表明,算法具有对视角与遮挡的鲁棒性和较高的检测速度. -The multi-scale orientation (MSO) features for pedestrian detection in still images are put forwarded in

this paper. Extracted on randomly sampled square image blocks (units), MSO features are made up of coarse and

fine features, which are calculated with a unit gradient and the Gabor wavelet magnitudes respectively. Greedy

methods are employed respectively to select the features.　Furthermore, the selected features are inputted into a

cascade classifier with Adaboost and SVM for classification. In addition, the spatial location of MSO units can be

shifted, are used to the handle multi-view problem and assembled　therefore, the occluded features are completed

with average features of training positives, given an occlusion model, which enable the proposed approach to work

in crowd scenes. Experimental results on INRIA testset and SDL multi-view testset report the state-of-arts results on

INRIA include it is 12.4 times the faster than SVM+HOG method.