Human pose estimation based on human limbs

Guoqiang Liang, Xuguang Lan, Jiang Wang, Nanning Zheng

Abstract: Modeling the relationship among human joints is one of the most important components in human pose estimation. Previous methods usually define this relationship as geometric constraints on the relative location of two neighboring joints. In this definition, the local image appearance of the region connecting two neighboring joints is ignored. In fact, this image appearance, called human limb, plays an important role in human joint localization in human visual system. To make full use of this local image appearance, we propose to solve a new task: human limb detection. We combine it with human joint localization in one deep convolutional neural network. After getting coarse results, we employ a graphical model to remove false positive detections. Besides, shallow and deep features are combined in this model. We evaluate our method on the FLIC and LSP datasets. The experiments results show the effectiveness of our method.