结合形状约束的Graph Cut行人分割

传统的Graph Cut算法没有对目标的形状予以限制，很难得到语义化的分割结果，即无法保证分割出来的是“行人”。针对该问题提出一种结合形状和底层特征的Graph Cut算法。对于行人分割，用大量真实行人轮廓来表达“行人”的先验形状，对Graph Cut分割算法予以约束，同时构建一个行人模板的层次树以减少匹配时间；并且提出一种区分性的外观模型来替换原来的外观模型。实验结果证明，该算法的分割结果明显优于传统Graph Cut算法的分割结果，所得到的轮廓与真实的行人轮廓比较吻合。

Pedestrian segmentation based on Graph Cut with shape prior

Most of the variants of Graph Cut algorithm do not impose any shape constraints on the segmentations, rendering it difficult to obtain semantic valid segmentation results. As for pedestrian segmentation, this difficulty leads to the non-human shape of the segmented object. An improved Graph Cut algorithm combining shape priors and discriminatively learned appearance model was proposed in this paper to segment pedestrians in static images. In this approach, a large number of real pedestrian silhouettes were used to encode the a'priori shape of pedestrians, and a hierarchical model of pedestrian template was built to reduce the matching time, which would hopefully bias the segmentation results to be humanlike. A discriminative appearance model of the pedestrian was also proposed in this paper to better distinguish persons from the background. The experimental results verify the improved performance of this approach.