CarGait: Cross-Attention Based Re-Ranking for Gait Recognition

Abstract

Gait recognition is a computer vision task that identifies individuals based on their walking patterns. Its performance is commonly evaluated by ranking a gallery of candidates and measuring the identification accuracy at Rank-K. Existing models are typically single-staged, searching for the probe's nearest neighbors in a gallery, using a global feature representation. While these models can excel at retrieving the correct identity within the top-K predictions, they often struggle when hard negatives are among the top shortlist, leading to relatively low performance at the highest ranks (e.g., Rank-1). In this paper, we introduce CarGait, a Re-ranking (re-ordering the top-K list) method for gait recognition, leveraging the fine-grained correlations between pairs of gait sequences, through cross-attention between gait strips. This re-ranking scheme can be adapted to existing single-stage models to enhance their final results. We demonstrate the capabilities of CarGait by extensive experiments on three common gait datasets, Gait3D, GREW, and OU-MVLP, and seven different gait models, showing consistent gains in Rank-1,5 accuracy, while outperforming existing re-ranking approaches, and a strong baseline.