Abstract: Since the multi-person pose estimation in crowded scenes is still affected by the problems of small detection objects, resulting in low accuracy of pose estimation, this paper proposes a multi- person pose estimation correction algorithm based on improved YOLOv5. Firstly, in the backbone network of YOLOv5, a jump attention module is integrated to help the network find the region of interest in the image. Secondly, in the neck network, the weighted bidirectional feature pyramid is used to improve the feature fusion ability between feature maps of different scales, and the jump attention module and Transformer encoder are used jointly to enable the network to obtain global information and rich context information. Thirdly, a detection head is added to the detection part to make the network more sensitive to tiny objects. Finally, the key point object information obtained by network prediction is used to modify the attitude object information to obtain the final multi-person pose estimation result. Experimental results show that the proposed algorithm improves YOLOv5s AP50 by 2.2% and AP75 by 3.3% on the COCO dataset, validating the accuracy and robustness of this algorithm.

Key words: person pose estimation, jump attention mechanism, weighted feature pyramid, Transformer encoder, object detection