Gait recognition holds the promise to robustly identify subjects based on
walking patterns instead of appearance information. In recent years, this field
has been dominated by learning methods based on two principal input
representations: dense silhouette masks or sparse pose keypoints. In this work,
we propose a novel, point-based Contour-Pose representation, which compactly
expresses both body shape and body parts information. We further propose a
local-to-global architecture, called GaitContour, to leverage this novel
representation and efficiently compute subject embedding in two stages. The
first stage consists of a local transformer that extracts features from five
different body regions. The second stage then aggregates the regional features
to estimate a global human gait representation. Such a design significantly
reduces the complexity of the attention operation and improves efficiency and
performance simultaneously. Through large scale experiments, GaitContour is
shown to perform significantly better than previous point-based methods, while
also being significantly more efficient than silhouette-based methods. On
challenging datasets with significant distractors, GaitContour can even
outperform silhouette-based methods.

通过提出一种新的基于点的 Contour-Pose 表示方法和一种局部到全局的架构，本文在步态识别领域表现出更好的性能和更高的效率，进一步证明在具有显著干扰的数据集上超过轮廓点方法的成果。