计算机工程2025，Vol.51Issue(10)：97-110,14.DOI:10.19678/j.issn.1000-3428.0069738
教学场景下基于几何关系感知的人体姿态估计表示学习模型
Geometry Relationship-aware Representation Learning Model for Human Pose Estimation in Teaching Scenarios
摘要
Abstract
Human Pose Estimation(HPE)is an important research task in the field of computer vision and is widely used in teaching scenarios.Currently,this task faces many challenges,such as reduced accuracy in complex scenarios,including cluttered backgrounds,small human body image scales,and occluded human bodies.Simultaneously,the flexibility and variability of human body postures require the model to have a good reasoning ability.This study proposes a geometric relationship-aware human pose representation learning model to address these problems.It uses the structured information of the human body to help the model better understand the relationship between different poses,thereby improving the accuracy and robustness of complex pose predictions to achieve effective application in classroom scenarios.The model includes four modules:channel reweighting,multi-token information interaction,limb direction construction,and adaptive loss propagation.The limb direction construction module implements the modeling of the geometric structure between the human body joints.This input clue helps the model capture the relative position and direction relationship between body parts.The channel reweighting module automatically selects and emphasizes the most helpful feature information for the pose estimation task,improving the expression ability of the visual features of the input image.The multi-token information interaction module,which is based on the Transformer encoder,realizes efficient interactions among image feature clues,joint coordinate clues,and limb direction cues.Finally,this study optimizes the traditional loss function in the adaptive loss propagation module to further improve the training effect and performance of the model.The model achieves accuracy rates of 76.1％and 90.3％on two mainstream datasets,COCO and MPII,respectively,outperforming some existing SOTA(State of the Art)models.The proposed model achieves more accurate and reasonable prediction results in complex scenarios.关键词
人体姿态估计/几何结构线索/肢体方向/Transformer/图像理解Key words
Human Pose Estimation(HPE)/geometry structure cue/limb direction/Transformer/image understanding分类
计算机与自动化引用本文复制引用
刘海,朱俊艳,张昭理,周启云,宋云霄..教学场景下基于几何关系感知的人体姿态估计表示学习模型[J].计算机工程,2025,51(10):97-110,14.基金项目
科技部2021年度"社会治理与智慧社会科技支撑"重点专项(2021YFC3340802) （2021YFC3340802）
国家自然科学基金(6247077114,62377037,62277041) （6247077114,62377037,62277041）
江西省自然科学基金(20242BAB2S107,20232BAB212026) （20242BAB2S107,20232BAB212026）
江西省高校教学改革研究项目(JXJG-23-27-6) （JXJG-23-27-6）
深圳市自然科学基金面上项目(JCYJ20230807152900001) （JCYJ20230807152900001）
湖北省自然科学基金创新发展联合基金项目(2025AFD621) （2025AFD621）
广东省基础与应用基础研究基金(2025A1515010266) （2025A1515010266）
2024年度湖北省教育厅科学技术研究计划项目(B2023300) （B2023300）
华中师范大学中央高校基本科研业务费专项资金(CCNU25ai012). （CCNU25ai012）
