Due to the depth ambiguity of RGB images,the hand joint depth coordinates are usually more difficult to estimate com-pared to the hand joint 2D image coordinates.A dual branch hand pose estimation algorithm based on multi-scale feature fusion was proposed.The FPN was used to extract multi-scale features of the hand.A feature fusion module was proposed to fuse and enhance the hand features,obtaining high-level and low-level features of the hand.A dual branch network structure was pro-posed,in which the high-level features and low-level features were used to estimate the depth coordinates and two-dimensional image coordinates of the hand joints,respectively.Sufficient experiments were conducted on two publicly available hand pose datasets.The proposed method achieves the best results in terms of the mean joint error metric compared with state-of-the-art methods.
维普
万方数据