HiH：一种用于无约束步态识别的多模态层级嵌套网络
HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition
AI总结：
HiH 提出一种融合剪影与姿态序列的多模态层级嵌套网络，通过层级步态分解、可变形空间增强和时间对齐模块，在无约束步态识别中实现精度与效率的平衡。
摘要：
步态识别在受控环境中已取得可观进展，但在无约束环境下仍面临显著困难，原因包括视角变化、遮挡以及行走速度变化等挑战。此外，融合多种模态的努力往往因跨模态不兼容而收益有限，尤其是在室外场景中。为解决这些问题，我们提出了一种多模态层级嵌套网络（HiH），该网络融合剪影序列和姿态序列以实现鲁棒的步态识别。HiH 设有一个主分支，利用层级步态分解器（HGD）模块对剪影数据中的一般步态模式进行逐深度和模块内的层级化分析。该方法能够捕获从整体身体动态到精细肢体运动的运动层级，从而促进跨多个空间分辨率的步态属性表示。作为补充，一个基于二维关节序列的辅助分支丰富了步态分析的空间和时间方面。该辅助分支采用可变形空间增强（DSE）模块进行姿态引导的空间注意力，并采用可变形时间对齐（DTA）模块通过学习的时间偏移来对齐运动动态。在多个室内和室外数据集上的广泛评估表明，HiH 达到了最先进的性能，并证实了其在准确性与效率之间取得了良好平衡。
英文摘要：
Gait recognition has achieved promising advances in controlled settings, yet it significantly struggles in unconstrained environments due to challenges such as view changes, occlusions, and varying walking speeds. Additionally, efforts to fuse multiple modalities often face limited improvements because of cross-modality incompatibility, particularly in outdoor scenarios. To address these issues, we present a multi-modal Hierarchy in Hierarchy network (HiH) that integrates silhouette and pose sequences for robust gait recognition. HiH features a main branch that utilizes Hierarchical Gait Decomposer (HGD) modules for depth-wise and intra-module hierarchical examination of general gait patterns from silhouette data. This approach captures motion hierarchies from overall body dynamics to detailed limb movements, facilitating the representation of gait attributes across multiple spatial resolutions. Complementing this, an auxiliary branch, based on 2D joint sequences, enriches the spatial and temporal aspects of gait analysis. It employs a Deformable Spatial Enhancement (DSE) module for pose-guided spatial attention and a Deformable Temporal Alignment (DTA) module for aligning motion dynamics through learned temporal offsets. Extensive evaluations across diverse indoor and outdoor datasets demonstrate HiH's state-of-the-art performance, affirming a well-balanced trade-off between accuracy and efficiency.
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