Cascade Feature Aggregation for Human Pose Estimation

Understand

Human pose estimation plays an important role in many computer vision tasks and has been studied for many decades.

• However, due to complex appearance variations from poses, illuminations, occlusions and low resolutions, it still remains a challenging problem.
• Taking the advantage of high-level semantic information from deep convolutional neural networks is an effective way to improve the accuracy of human pose estimation.
• In this paper, we propose a novel Cascade Feature Aggregation (CFA) method, which cascades several hourglass networks for robust human pose estimation.