基于残差网络和注意力机制的步态识别算法

A Gait Recognition Algorithm Based on Residual Network and Attention Mechanism

针对现有行人步态数据集样本较少、多特征融合复杂且识别精准度不高的问题,提出了一种基于残差网络和注意力机制的步态识别算法。首先对每种特征制作标签,然后将表示不同特征的标签做拼接,达到识别多特征的目的。通过迁移ResNet18的预训练模型,并在ResNet18网络第一个和最后一个卷积层后面加入注意力机制,能够加速网络的收敛,提升模型的精准率。在CASIA-B数据集上进行多次实验,结果表明所提出方法能准确识别行人、状态和不同角度三种步态特征,识别的精准率为97.6%。

For the problems of few samples,complex multi-feature fusion and low recognition accuracy in the existing pedestrian gait data sets,a gait recognition algorithm based on residual network and attention mechanism is proposed.Firstly,labels are made for each feature,and then the labels representing different features are stitched to achieve the purpose of identifying multiple features.By migrating the pre-training model of ResNet18 and adding an attention mechanism after the first and last convolutional layers of the ResNet18 network,the convergence of the network can be accelerated and the accuracy of the model can be improved.Experiments on the CASIA-B data set show that the proposed method can accurately identify three gait features of pedestrians,states,and different angles,and the recognition accuracy is 97.6%.