Postprocessing of human pose heatmap based on sub-pixel location
To improve the prediction accuracy of joint points of the heatmap,this paper proposes a postprocessing method of human pose heatmap based on sub-pixel localization.The method includes two strategies:the first is the sub-pixel shift processing of the flipped image heatmap,which can eliminate the unaligned deviation from the original image heatmap;the second is the heatmap decoding for local region surface fitting to achieve sub-pixel localization of the joint points.The heatmap postprocessing method in this paper is independent of the network model and can be applied to the current heatmap-based human pose estimation models without any modification.To verify the effectiveness of the proposed method,experiments have been carried out by using two publicly available datasets named COCO2017 and MPII.The average precision can be improved by 0.9 and 1.1 on COCO2017,respectively,by adopting two deep learning models,i.e.,HRNet-W32-256×192 model and Simple Baseline-W32-256×192 model.
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