国家自然科学基金(61039003, 60872143); 国家重点基础研究发展计划(973)(2011CB706900, 2010CB731800)
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
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.