Correlating Edge with Parsing for Human Parsing

Human parsing has great application prospects in the field of computer vision, but there are still many problems. In the existing algorithms, the problems of small-scale target location and the problem of background occlusion have not been fully resolved, which will lead to wrong segmentation or incomplete segmentation. Compared with the existing practice of feature concatenation, using the correlation between two factors can make full use of edge information for refined parsing. This paper proposes the mechanism of correlation edge and parsing network (MCEP), which uses the spatial aware and two max-pooling (SMP) module to capture the correlation. The structure mainly includes two steps, namely (1) collection operation, where, through the mutual promotion of edge features and parsing features, more attention is paid to the region of interest around the edge of the human body, and the spatial clues of the human body are collected adaptively, and (2) filtering operation, where parallel max-pooling is adopted to solve the background occlusion problem. Meanwhile, semantic context feature extraction capability is endowed to enhance feature extraction capability and prevent small target detail loss. Through a large number of experiments on multiple single-person and multi-person datasets, this method has greater advantages.