关键词: 人体解析, 空洞卷积, 编码解码网络, 深度学习

Abstract:

Human parsing, as a complex and delicate computer vision task, has a wide application prospect. In order to obtain accurate human parsing results, it is necessary to extract rich human semantic features. In this paper, a two-way human parsing network（MTCnet） is proposed. MTCnet combines the encoding and decoding network with atrous convolution, it has two feature extraction subnets that can integrate the learning of multi-scale feature information. Compared with the single network, it can learn richer semantic feature information of human. Different from the previous one-step approach, the method proposed in this paper needs to carry out multi-stage learning. Each stage improves the human parsing results of the previous stage, and finally achieves the optimal result. The experimental results show that the proposed method has stronger feature extraction ability and more accurate human parsing results than some advanced methods.

Key words: human parsing, atrous convolution, encoder-decoder network, deep learning