LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Exploring hard joints mining via hourglass-based generative adversarial network for human pose estimation

Photo from wikipedia

Human pose estimation has broad application prospects in the fields of human behavior recognition and human-computer interaction. Although the current human pose estimation methods have made tremendous progress, the partial… Click to show full abstract

Human pose estimation has broad application prospects in the fields of human behavior recognition and human-computer interaction. Although the current human pose estimation methods have made tremendous progress, the partial occlusion of human bodies still remains a challenging problem. In this paper, we address the challenging joints in human bodies by the hard joints mining technique. The proposed hard joints mining method is based on the generative adversarial network, which consists of two stacked hourglasses with a similar architecture: the generator and the discriminator. During the training period, the discriminator distinguishes the generated heatmaps from the ground-truth heatmaps and introduces the adversarial loss to the generator through back-propagation to induce generator generates a more reasonable prediction. Moreover, the hard joints mining technique is used to focus the training attention on the difficult joint points in the generator. Finally, the experimental results demonstrate the effectiveness of the proposed approach for human pose estimation on Leeds Sports Pose (LSP) Dataset, LSP-extended datasets and MPII Human Pose Datasets.

Keywords: hard joints; joints mining; human pose; based generative; pose estimation

Journal Title: AIP Advances
Year Published: 2019

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.