GaitTransformer: Multiple-Temporal-Scale Transformer for Cross-View Gait Recognition
Yufeng Cui, Yimei Kang
Abstract
Gait recognition aims to identify different people by walking patterns in a long-distance. At present, most gait recognition methods mainly focus on short-term temporal frame-level feature modeling, while long-term temporal relations and some prior information such as view angle, walking condition are not fully exploited. To alleviate this issue, we propose a transformer-based gait recognition framework called GaitTransformer. A Multiple-Temporal-Scale Transformer (MTST), which consists of multiple transformer encoders with multi-scale position embedding is proposed for the framework to integrate various long-term information of the sequence. Moreover, we further design a knowledge embedding for the MTST to model prior knowledge information. Experiments demonstrate that our GaitTransformer achieves state-of-the-art performance on popular gait datasets.