Research on Multi Person Pose Estimation Network Based on Improved Attention Mechanism
To solve the problem of low accuracy in locating small and medium-sized joint points in multi person pose estimation,an improved multi person pose estimation network based on a stacked hourglass network was proposed using a top-down approach combined with the human object detection model YOLOv4-tiny.This network included a modified YOLOv4-tiny(MYT)and a coordinate-stacked hourglass networks(COD-SHN)algorithm.The features were enhanced by incorporating coordinate attention mechanism into the original residual module of the hourglass network,which suppressed useless features while enhancing useful ones.Therefore,the recognition accuracy of small and medium-sized human joints was improved.The experimental results showed that the model achieved an average precision of 64.9％on COCO dataset,and the percentage of correct keypoints on MPII dataset reached 88.8％,indicating the effectiveness of the network.
human pose estimationhuman target detectionattention mechanismstacked hourglass network
万方数据
维普
