电子学报2024，Vol.52Issue(5)：1730-1742,13.DOI:10.12263/DZXB.20230729
基于多尺度增量学习的单人体操动作中关键点检测方法
Keypoint Detection Method for Single Person Gymnastics Actions Based on Multi-Scale Incremental Learning
摘要
Abstract
Keypoint detection of human body is a hot research area in computer vision.At present there exist some problems for keypoint detection in gymnastics actions,such as insufficient detection accuracy and lack of capability to de-tect detailed body parts.In order to improve the detection accuracy,this paper proposes a multi-resolution network that has a larger receptive field in the shallow layers and can utilize high-resolution channel to enhance the extraction of detailed fea-tures.To achieve the detection of keypoints of hands and feet,an incremental learning network is designed.The network fuses the shallow features of the multi-resolution network and computes deep features using a gymnastics actions self-built dataset,so that the detection ability of keypoints on hands and feet is improved.Finally,the output results of the two sub-networks are concated.Computer simulations demonstrate that the multi-resolution network achieves an accuracy rate of 94.4%on the COCO2017 keypoint detection dataset,and the incremental learning network can accurately detect keypoints of detailed body parts with fewer training data.关键词
人体关键点检测/体操动作/多分辨率网络/增量学习/权重融合Key words
human keypoint detection/gymnastics actions/multi-resolution network/incremental learning/weight fusion分类
信息技术与安全科学引用本文复制引用
江佳鸿,夏楠,李长吾,周思瑶,于鑫淼..基于多尺度增量学习的单人体操动作中关键点检测方法[J].电子学报,2024,52(5):1730-1742,13.基金项目
教育部产学合作协同育人项目(No.220603231024713) Ministry of Education Industry-University Cooperation and Collaborative Education Project(No.220603231024713) （No.220603231024713）
