As a key technology capable of extracting structural and behavioral features of the human body, pose estimation is increasingly becoming an important addition and development direction in the field of biometrics. The current human pose estimation network faces two major problems in practical applications: first, the number of parameters is large and the computational overhead is high; second, the detection accuracy is insufficient, especially in complex scenarios such as multi-people, which can easily lead to key point localization bias due to occlusion. Aiming at the above two aspects, in this paper, we design a high-precision and lightweight deep learning network, HG-Ghost, and at the same time, in order to solve the occlusion problem and enhance the network’s ability to capture the region of interest, we introduce CA, and furthermore, an online convolutional reparametrization module is integrated into the Neck structure of the network in order to reduce the model’s memory occupation and training cost. Experimental evaluations on the COCO2017 and Human3.6 m datasets show that compared with the baseline model YOLOv8-Pose, our model reduces the number of parameters by 25%, reduces the amount of floating-point operations by 18%, and improves the accuracy by 4.5 percentage points, which fully demonstrates the significant advantages of the improved model in this paper in the field of pose estimation.