Human pose estimation constrained by human structure information

We present a two-dimensional human pose estimation network constrained by the human structure information (HSINet). HSINet effectively fuses features of different scales and explicitly integrates human structure information to enhance the precision of key point localization. The architecture of HSINet comprises three pivotal modules: the feature extraction module, the encoding module, and the decoding module. The feature extraction module within HSINet employs the architecture of High-Resolution Net (HRNet). In contrast to HRNet, we remove redundant layers, and enhance the ability to combine global features and local features using the Gated Attention Unit (GAU). The second module encodes the feature maps derived from the feature extraction module. Each feature map corresponds to a joint point and is characterized by two feature vectors representing the x and y axes. Utilizing graph convolution for encoding introduces constraints based on human structure information. Subsequently, these encoded feature maps are decoded into precise coordinates of key points. The experiment results on COCO datasets show that our proposed method can improve the precision of key point detection while effectively reducing the number of parameters.