SkeletonGait：基于骨架图的步态识别
SkeletonGait: Gait Recognition Using Skeleton Maps
AI总结：
针对步态识别中轮廓表示场景受限、骨架结构信息利用不足的问题，提出骨架图表示与SkeletonGait方法，进一步构建多分支SkeletonGait++融合两类特征，在多数据集上取得最优性能。
摘要：
表示方法的选择对深度步态识别方法至关重要。二值轮廓和骨架坐标是近年文献中两种主流表示方式，在诸多场景下取得了显著进展。但二者仍存在固有挑战：轮廓在无约束场景中并非总能获取，而骨架中的结构线索尚未得到充分利用。本文提出一种名为骨架图（skeleton map）的新型骨架步态表示方法，以及SkeletonGait——一种从人体骨架图中挖掘结构信息的基于骨架的方法。具体而言，骨架图通过高斯近似将人体关节坐标表示为热图，呈现出类似轮廓、却不包含精确身体结构的图像形式。该方法不仅在5个主流步态数据集上取得了当前最优（SOTA）性能，更重要的是，SkeletonGait揭示了关于结构特征在步态描述中的重要性及其作用时机的全新洞见。此外，我们提出了名为SkeletonGait++的多分支架构，以融合利用骨架与轮廓的互补特征。实验表明，SkeletonGait++在多种场景下均大幅优于现有最优方法，例如在具有挑战性的GREW数据集上，其rank-1准确率超过85%，表现出色。所有源代码均公开于https://github.com/ShiqiYu/OpenGait。
英文摘要：
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 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 is available at https://github.com/ShiqiYu/OpenGait.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
