Abstract: Human pose estimation plays a vital role in human-computer interaction and behavior recognition applications, but the changing scale of feature maps poses a challenge to the relevant methods in predicting the correct human poses. In order to heighten the accuracy of pose estimation, the method for the parallel network multi-scale fusion and that for generating high-quality feature maps were combined for human pose estimation. On the basis of human detection, RefinedHRNet adopted the method for parallel network multi-scale fusion to expand the receptive field in the stage using a dilated convolution module to maintain context information. In addition, RefinedHRNet employed a deconvolution module and an up-sampling module between stages to generate high-quality feature maps. Then, the parallel network feature maps with the highest resolution (1/4 of the input image size) were utilized for pose estimation. Finally, Object Keypoint Similarity (OKS) was used to evaluate the accuracy of keypoint recognition. Experimenting on the COCO2017 test set, the pose estimation accuracy of our proposed method RefinedHRNet is 0.4% higher than the HRNet network model. 

Key words: pose estimation, multi-scale fusion, high-quality feature maps, human detection, object keypoint similarity