Multi-view Gait Recognition based on Siamese Vision Transformer
This work addresses gait recognition across different camera views, which is important for surveillance and biometrics, but it appears incremental as it adapts existing transformer methods to a specific domain.
The paper tackled multi-view gait recognition by proposing a Siamese Mobile Vision Transformer (SMViT) to handle view variations, achieving a 96.4% average recognition rate on the CASIA B dataset.
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.