Human pose estimation based on Improved High Resolution Network. Issue 1 (July 2021) - Bao, Ying; Zhang, Manlin; Guo, Xiaoming
Abstract: Human pose estimation has become a hot problem in human-computer interaction and other intelligent application technologies, but the results obtained by previous deep learning networks are not very satisfactory when facing small-scale human instances. Therefore, in order to solve the problem of scale variation in human pose estimation, especially to pinpoint the keypoints of small-scale human instances, an improved High-Resolution Network (Improved HRNet) is proposed in this paper. The main improvement work in this paper is as follows: In this paper, a double attention mechanism is added in the forward transmission of the parallel sub-network, with the aim of assigning weights to the propagated information without changing the number of channels, assigning the information with high weights as useful information and reducing the interference caused by irrelevant information. In this paper, the network structure is validated using COCO dataset, and the average accuracy (Average Precision, AP) of Improved HRNet is 66.4, which is 2.3 higher than the average accuracy of High-Resolution Network (HRNet) with only 0.4% increase in parameters.