Cycle Fusion Network for Multi-Person Pose Estimation
Yuanxiang Wang, Teng Wang · Journal of Physics Conference Series · 2020
Abstract Multi-person pose estimation has been largely improved with the development of convolutional neural network, which however remains a challenging task in computer vision. The challenge is particularly pronounced in high flexibility of occlusion, camera angles, imaging-caused body limbs. Existing methods are mostly based on networks with deep architectures, which are capable of inferring the semantic labels with high accuracy while suffering from positioning accuracy degeneration caused by the loss of the spatial information. To address these problems, we present a novel network called cycle fusion network to improve the pose estimation accuracy in multi-person environments. The cycle fusion network has a two-stage structure. In the first stage, a cycle-path feature fusion is used to increase the semantic information for low level layers and the spatial information for high level layers respectively. In the second stage, a cross-scale feature fusion is used for refining the positions of keypoints. Equipped together with RFB module and multi-level supervision, the proposed method has shown the state-of-the-art performance on the large-scale MS COCO benchmark with extensive experiments.