论文标题
基于暹罗视觉变压器的多视图步态识别
Multi-view Gait Recognition based on Siamese Vision Transformer
论文作者
论文摘要
尽管视觉变压器已用于步态识别，但其在多视图步态识别中的应用仍然有限。不同的观点显着影响步态轮廓特征的提取和识别精度。为了解决这个问题，本文提出了暹罗移动视觉变压器（SMVIT）。该模型不仅侧重于人步态空间的局部特征，而且还考虑了长距离注意关联的特征，这些特征可以提取多维步骤状态特征。此外，它描述了不同的观点如何影响步态特征并产生可靠的观点特征关系因素。 CASIA B数据集对SMVIT的平均识别率达到96.4％。实验结果表明，与诸如Gaitgan，Multi_view Gan，Posegait和其他步态识别模型等先进的步骤识别模型相比，SMVIT可以达到最先进的性能。
While the Vision Transformer has been used in gait recognition, its application in multi-view gait recognition is still limited. Different views significantly affect the extraction and identification accuracy of the characteristics of gait contour. To address this, this paper proposes a Siamese Mobile Vision Transformer (SMViT). This model not only focuses on the local characteristics of the human gait space but also considers the characteristics of long-distance attention associations, which can extract multi-dimensional step status characteristics. In addition, it describes how different perspectives affect gait characteristics and generate reliable perspective feature relationship factors. The average recognition rate of SMViT on the CASIA B data set reached 96.4%. The experimental results show that SMViT can attain state-of-the-art performance compared to advanced step recognition models such as GaitGAN, Multi_view GAN, Posegait and other gait recognition models.
