基于自适应人体拓扑结构引导的步态识别

Adaptive Human Body Topology Guidance for Gait Recognition

不同于基于外形的步态识别方法,基于关键点的步态识别方法采取人体关键点作为模型的输入,能够有效避免数据集带来的背景噪声干扰;其次,现有的基于关键点的步态识别方法忽略了人体结构先验知识的利用,且更倾向于提取局部特征,从而忽略了全局上的关联性.本文提出了一个基于关键点的步态识别框架GaitBody,能够从步态关键点序列中提取更有分辨性的特征.首先,我们设计了带有较大卷积核的多尺度卷积模块来提取多粒度的时序特征;其次,我们利用自注意力机制来提取空间特征,并在此基础上引入了人体结构拓扑信息来进一步利用人体结构的先验知识;最后,为了更好使用时序信息,我们生成最有代表性的时序特征,并将其引入到自注意模块来融合时序和空间特征.在CASIA-B和OUMVLP-Pose数据集上的实验结果表明,我们的方法在基于关键点的步态识别方法上取得了最优结果,消融实验也证明了各个模块的有效性.

Unlike appearance-based methods whose input may bring in some background noises,skeleton-based gait representation methods take key joints as input,which can neglect the noise interference.Meanwhile,most of the skeleton-based representation methods ignore the significance of the prior knowledge of human body structure or tend to focus on the local features.This study proposes a skeleton-based gait recognition framework,GaitBody,to capture more distinctive features from the gait sequences.Firstly,the study leverages a temporal multi-scale convolution module with a large kernel size to learn the multi-granularity temporal information.Secondly,it introduces topology information of the human body into a self-attention mechanism to exploit the spatial representations.Moreover,to make full use of temporal information,the most salient temporal information is generated and introduced into the self-attention mechanism.Experiments on the CASIA-B and OUMVLP-Pose datasets show that the method achieves state-of-the-art performance in skeleton-based gait recognition,and ablation studies show the effectiveness of the proposed modules.