基于帧级-时空双分支网络的步态识别方法

Gait Recognition Based on Frame⁃Level and Spatio⁃Temporal Double Branch Network

针对现有的步态识别方法未能充分利用帧级特征信息和时空特征信息的问题,提出了基于帧级-时空双分支网络的步态识别方法。该网络是一个新的双分支结构,包括帧级特征提取和时空特征提取分支。为了进一步增强特征表示,设计了一个时序集成模块连接2个分支结构,使帧级特征提取分支的信息能够多次集成于时空特征提取分支。所提方法在CASIA⁃B和OU⁃MVLP数据集上进行了实验。实验结果表明,所提方法在正常行走和复杂条件下都表现出很好的性能。

Existing gait recognition methods fail to make full use of frame⁃level feature information and spatio⁃temporal feature information.To solve this issne,a novel frame and temporal⁃spatial gait network based on frame⁃spatio⁃temporal double branch network is proposed,which includes two branches,the one is frame⁃level feature extraction,and the other is spatial⁃temporal feature extraction.Then,a temporal integration module is proposed to bridge two branches,which enables the information of frame⁃level feature extraction branch to be integrated with the spatial⁃temporal feature extraction branch many times and enhances the capability of feature representation.The proposed gait recognition network experiments on popular gait recognition datasets CASIA⁃B and OU⁃MVLP.The results of experiments verify the effectiveness of the proposed method under normal walking and complex conditions.