Multi-person Pose Estimation Under Complex Environment Based on Progressive Rotation Correction and Multi-scale Feature Fusion

The research of multi-person pose estimation has been largely improved recently. However, multi-person pose estimation in complex environments is still challenging. For example, the following two situations cannot be handled well by existing pose estimation methods: first, there are pedestrians that are not upright or even inverted in the image, and pedestrians of different scales appear in the same image. To solve these problems, the Progressive rotation correction module (PRCM) and Scale-invariance module (SIM) based on multi-scale feature fusion are proposed. First of all, the PRCM was proposed to address the situation where pedestrians appear rotated or even inverted in the image. This module is divided into three stages, with the aim of gradually correcting the inverted human to an upright one. Besides, SIM is designed to handle multi-scale problems. In this module, dilated convolutions with different receptive field are used to extract multi-scale information. Then, the extracted multi-scale features (different semantic information in different feature maps) will be fused to solve the multi-scale problem. The experimental results show that our algorithm can reach an AP value of 72.0% when tested on the COCO2017 dataset. Demonstrates that the proposed method is superior to state-of-the-art methods.