CarGait: Cross-Attention based Re-ranking for Gait recognition

• URL: http://arxiv.org/abs/2503.03501v1
• Date: Wed, 05 Mar 2025 13:47:02 GMT
• Title: CarGait: Cross-Attention based Re-ranking for Gait recognition
• Authors: Gavriel Habib, Noa Barzilay, Or Shimshi, Rami Ben-Ari, Nir Darshan,
• Abstract summary: Gait recognition is a computer vision task that identifies individuals based on their walking patterns.<n>Existing models are typically single-staged, i.e. searching for the probe's nearest neighbors in a gallery.<n>We introduce CarGait, a Cross-Attention Re-ranking method for gait recognition.
• Score: 4.334105740533729
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Gait recognition is a computer vision task that identifies individuals based on their walking patterns. Gait recognition performance is commonly evaluated by ranking a gallery of candidates and measuring the accuracy at the top Rank-$K$. Existing models are typically single-staged, i.e. searching for the probe's nearest neighbors in a gallery using a single global feature representation. Although these models typically excel at retrieving the correct identity within the top-$K$ predictions, they struggle when hard negatives appear in the top short-list, leading to relatively low performance at the highest ranks (e.g., Rank-1). In this paper, we introduce CarGait, a Cross-Attention Re-ranking method for gait recognition, that involves re-ordering the top-$K$ list 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 improvements in Rank-1,5 accuracy, superior results over existing re-ranking methods, and strong baselines.

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