Correction Algorithm of Multi-Person Pose Estimation Based on Improved YOLOv5

Abstract

Since multi-person pose estimation in crowded scenes still suffers from problems such as small detection targets, resulting in low pose estimation accuracy, we propose a correction algorithm for multi-person pose estimation based on the improved YOLOv5. Firstly, in the backbone network of YOLOv5, the Jumping Attention Mechanism module is incorporated to help the networks find the region of interest in the images; Secondly, in the neck network, the joint use of the Jump Attention Mechanism module and the Transformer encoder allows the network to acquire global information and contextual information. Finally, the key point object information obtained from the network prediction is used to correct the pose object information to get the final multi-person pose estimation results. The experimental results show that the method in this paper improves AP50 by 2.2% and AP75 by 3.3% over YOLOv5 on the COCO dataset, which verifies the accuracy and robustness of the approach in this paper.