BalanceHRNet: An effective network for bottom-up human pose estimation.

Abstract

In the study of human pose estimation, which is widely used in safety and sports scenes, the performance of deep learning methods is greatly reduced in high overlap rate and crowded scenes. Therefore, we propose a bottom-up model, called BalanceHRNet, which is based on balanced high-resolution module and a new branch attention module. BalanceHRNet draws on the multi-branch structure and fusion method of a popular model HigherHRNet. And our model overcomes the shortcoming of HigherHRNet that cannot obtain a large enough receptive field. Specifically, through the connecting structure in balanced high-resolution module, we can connect almost all convolutional layers and obtain a sufficiently large receptive field. At the same time, the multi-resolution representation can be maintained due to the use of balanced high-resolution module, which enable our model to recognize objects with richer scales and obtain more complex semantics information. And for branch fusion method, we design branch attention to obtain the importance of different branches at different stages. Finally, our model improves the accuracy while ensuring a smaller amount of computation than HigherHRNet. The CrowdPose dataset is used as test dataset, and HigherHRNet, AlphaPose, OpenPose and so on are taken as comparison models. The AP measured by BalanceHRNet is 63.0%, increased by 3.1% compared to best model - HigherHRNet. We also demonstrate the effectiveness of our network through the COCO(2017) keypoint detection dataset. Compared with HigherHRNet-w32, the AP of our model is improved by 1.6%.