Human Parsing via Shape Boltzmann Machine Networks

Qiurui Wang, Chun Yuan, Feiyue Huang, Chengjie Wang

Abstract: Human parsing is a challenging task because it is difficult to obtain accurate results of each part of human body. Precious Boltzmann Machine based methods reach good results on segmentation but are poor expression on human parts. In this paper, an approach is presented that exploits Shape Boltzmann Machine networks to improve the accuracy of human body parsing. The proposed Curve Correction method refines the final segmentation results. Experimental results show that the proposed method achieves good performance in body parsing, measured by Average Pixel Accuracy (aPA) against state-of-the-art methods on Penn-Fudan Pedestrians dataset and Pedestrian Parsing Surveillance Scenes dataset.