计算机工程2024，Vol.50Issue(3)：317-325,9.DOI:10.19678/j.issn.1000-3428.0067134
基于注意力机制的人体关键点隐式建模网络
Implicit Modeling Network of Human Keypoints Based on Attention Mechanism
摘要
Abstract
Human pose estimation necessitates the use of visual cues and anatomical joint relationships to pinpoint key points.Existing Convolutional Neural Network(CNN)methods falter in addressing long-range contextual cues and modeling dependencies among distant joints.This paper introduces an attention-based implicit modeling method that iteratively computes feature correlations between joints,thus implicitly modeling the constraint relationships among key points.This method diverges from the localized operations characteristic of CNN by expanding the network's receptive field and modeling dependencies between distantly positioned joints.To counteract the diminished visibility of crucial keypoints during network training,a focal loss function is implemented,prompting the network to concentrate on complex keypoints.Comparative experiments were performed under identical conditions using the state-of-the-art High-Resolution Network(HRNet)and the classic Residual Network(ResNet)as backbone networks.Results reveal that the implicit modeling network enhances human pose estimation performance.For instance,utilizing HRNet as the backbone,the algorithm's accuracy on the MPII and MSCOCO human pose estimation benchmark datasets improved by 1.7%and 2.6%,respectively,surpassing the original network's performance.关键词
人体姿态估计/卷积神经网络/注意力机制/焦点损失/隐式建模Key words
human pose estimation/convolutional neural network/attention mechanism/focal loss/implicit modeling分类
信息技术与安全科学引用本文复制引用
赵佳圆,张玉茹,苏晓东,徐红岩,李世洲,张玉荣..基于注意力机制的人体关键点隐式建模网络[J].计算机工程,2024,50(3):317-325,9.基金项目
黑龙江省自然科学基金(LH2022F035) （LH2022F035）
2022年哈尔滨商业大学教师"创新"项目支持计划项目(XL0068) （XL0068）
哈尔滨商业大学研究生创新科研项目(YJSCX2022-743HSD). （YJSCX2022-743HSD）
