一种时空卷积的步态识别方法

Gait recognition method based on spatial-temporal convolution

由于步态识别的准确率会受到外部因素的影响,例如拍摄角度、行人穿着以及携带包的状态变化等,所以提出基于时空卷积的步态识别算法。该方法通过卷积神经网络提取步态特征,并通过对相邻帧进行重复提取来弥补当前帧丢失的信息,从而在步态轮廓序列图里挖掘更丰富的时空信息。最后,在CASIA-B数据集上对所提出的方法进行验证。实验结果表明,所提出的方法在正常行走、携带包和穿外套的情况下,Rank-1准确度都得到了很大的提升。

As a unique biological characteristic that can be recognized from a distance,gait has a wide application prospect in the fields of identity identification,public security and medical diagnosis.However,the accuracy of gait recognition will be affected by external factors,such as the shooting angle,the pedestrian’s wearing and the change in the status of carrying a bag.Based on the above problems,this paper puts forward the gait recognition method based on spatial-temporal convolution,which uses the convolution neural network to extract gait features,and employs the repeated extraction of the gait features of adjacent frames to make up for the missing information,so that more spatial-temporal information can be obtained.Finally,the proposed method is validated on the CASIA-B dataset.Experimental results show that the proposed method can improve the rank-1 accuracy when a pedestrian walks normally,carrying a bag and wearing a coat.