It is difficult to predict the correct human poses when facing the challenge of the scale change of the feature map in the human pose estimation. To solve this problem, this paper proposed a high-resolution network MSANet(multiscale-attention net) based on multi-scale attention mechanism to improve the detection accuracy of human pose estimation. It introduced lightweight pyramid convolution and attention feature fusion to achieve more efficient extraction of multi-scale information, cited the self-transformer module in the fusion of parallel subnets for feature enhancement to obtain global features. In the output stage, the features of each layer were fused using an adaptive spatial feature fusion strategy as the final output, which more fully obtained the semantic information of high-level features and the fine-grained features of low-level features to infer invisible points and occluded key points. Tested on the public dataset COCO2017, the experimental results show that this method improves the estimation accuracy by 4.2% compared with the basic network HRNet.

[2022-05-18] Accepted Paper /doc/2022.03.0109/v1

[2022-11-05] Printed Article /doc/2022.03.0109/v2