基于非局部稀疏特征的行人检测方法

利用周围邻域信息约束进行加权稀疏表示以达到行人检测的目的.采用Fisher判别字典学习的方法,得到一个能够更好地提取图像的具有更强辨别性稀疏特征的字典,利用图像中周围信息约束,求得该字典表示下的稀疏特征,并根据对当前图像块的稀疏表示残差进行分类.INRIA数据库的实验表明非局部稀疏特征具有明显的区分能力.同时,对行人目标进行邻域约束,能够有效地表示出同目标区域的稀疏特征.

Pedestrian Detection Based on Nonlocal Sparse Feature

By using the constraints around the neighborhoods for weighted sparse representation， the pedestrian detection problem was solved. A dictionary with a strong extracting discriminate and sparse features power was obtained by using the Fisher discriminant dictionary learning method. With the constraint of the neighborhoods， the image patch was represented as a sparse feature via the dictionary. By computing the representation of the residuals and comparing the residuals with a threshold， the patch label was determined to finish the classification task. The experiments on INRIA person datasets showed that non-local sparse feature has an obvious power of discrimination. The constraint of the neighborhoods makes the sparse feature represented effectively.