Abstract: Human pose estimation is the basis of many tasks in the field of computer vision. Due to the challenge of scale change, the previous human pose estimation network will lose pose information in the process of feature extraction, which makes it difficult to improve the accuracy of human pose estimation. To solve this problem, a parallel network combined with multi-scale feature fusion method is considered to extract features. The human posture estimation method for optimizing feature extraction is divided into two steps: firstly, in the multi-scale feature fusion stage, transpose convolution and mixed dilated convolution are used to reduce the loss of feature information. Secondly, in the feature map output stage, weighted feature maps of different scales are combined to eliminate redundant information, retain posture information, and generate higher quality high-resolution heat map at the same time. Experiments show that the accuracy of this method is improved by 2.1% compared with the advanced method HRnet (High Resolution Net). Experiments show that this method can surpass the existing mainstream human pose estimation methods in accuracy. This method can better meet the challenge of mesoscale change in pedestrian pose estimation, and more accurately locate the key points of small-scale human body in complex scenes.

Key words: pose estimation, multi-scale fusion, human detection, scale adaptation