现代电子技术2024，Vol.47Issue(10)：79-85,7.DOI:10.16652/j.issn.1004-373x.2024.10.015
基于加权DTW的多特征分级融合动态手势识别
Multi-feature hierarchical fusion for dynamic gesture recognition based on DTW
摘要
Abstract
Hand angle variation in dynamic motions captured from a first-person perspective is a common problem for augmented reality(AR)systems.Existing palm key point displacement features are ineffective for recognition because different key points'displacement trajectories differ from the frontal perspective.AR device limits also impose performance limitations for some neural network models.A gesture recognition method using multi feature hierarchical fusion is proposed for this application scenario.In this method,the displacement,length,and angle features are constructed to describe gestures,and vector encoding and normalization are conformed to eliminate jitter interference.Based on the similarity distance between the joint points and the standard gesture,the joint weights are assigned by means of the sigmoid function,and KNN matching is performed by means of the weighted dynamic time warming(DTW)distance.The most reliable feature recognition result is selected based on the best KNN confidence and feature priority.The experimental results show that this method can effectively recognize 9 dynamic gestures with angle deviation;in comparison with the traditional displacement feature methods,this method has an average accuracy improvement of 4%and can effectively address the dynamic gesture recognition problem under palm deflection.关键词
动态手势识别/多特征融合/DTW算法/关节点/位移特征/KNN分类Key words
dynamic gesture recognition/multi feature fusion/DTW algorithm/joint points/displacement characteristics/KNN classification分类
信息技术与安全科学引用本文复制引用
陈潘,华杭波,孔明,梁晓瑜..基于加权DTW的多特征分级融合动态手势识别[J].现代电子技术,2024,47(10):79-85,7.基金项目
国家市场监督管理总局技术保障专项(2022YJ21) （2022YJ21）
浙江省市场监督管理局科技计划(全额自筹)项目(ZC2023057) （全额自筹）
