The choice of the representations is essential for deep gait recognition
methods. The binary silhouettes and skeletal coordinates are two dominant
representations in recent literature, achieving remarkable advances in many
scenarios. However, inherent challenges remain, in which silhouettes are not
always guaranteed in unconstrained scenes, and structural cues have not been
fully utilized from skeletons. In this paper, we introduce a novel skeletal
gait representation named Skeleton Map, together with SkeletonGait, a
skeleton-based method to exploit structural information from human skeleton
maps. Specifically, the skeleton map represents the coordinates of human joints
as a heatmap with Gaussian approximation, exhibiting a silhouette-like image
devoid of exact body structure. Beyond achieving state-of-the-art performances
over five popular gait datasets, more importantly, SkeletonGait uncovers novel
insights about how important structural features are in describing gait and
when do they play a role. Furthermore, we propose a multi-branch architecture,
named SkeletonGait++, to make use of complementary features from both skeletons
and silhouettes. Experiments indicate that SkeletonGait++ outperforms existing
state-of-the-art methods by a significant margin in various scenarios. For
instance, it achieves an impressive rank-1 accuracy of over $85\%$ on the
challenging GREW dataset. All the source code will be available at
this https URL

本文提出了一种名为骨骼图的新型骨骼步态表示方法，以及基于骨骼的 SkeletonGait 方法，利用人体骨骼图的结构信息。通过使用来自骨骼和轮廓的互补特征，SkeletonGait++ 在各种场景中超过了现有的最先进方法。