基于骨架的步态识别的显式时频动态
Explicit Time-Frequency Dynamics for Skeleton-Based Gait Recognition
AI总结：
本文提出Wavelet Feature Stream，通过时频分析增强骨架步态识别的动态信息，提升在外观变化下的识别性能，尤其在携带物品等场景下表现更优。
摘要：
基于骨架的步态识别器在建模空间配置方面表现优异，但往往未充分利用显式运动动态，这对外观变化至关重要。本文引入了一个即插即用的Wavelet Feature Stream，该方法通过连续小波变换将关节速度序列转换为多尺度scalograms，随后轻量级多尺度CNN学习判别性动态特征。所得描述符与骨架表示融合用于分类，无需修改骨架架构或增加监督。在CASIA-B数据集上，所提方法在强骨架基础上实现一致提升，并在附加到GaitMixer时达到新状态。改进在协变量偏移场景（如携带包或穿外套）中尤为显著，突显了显式时频建模与标准时空编码器的互补性。
英文摘要：
Skeleton-based gait recognizers excel at modeling spatial configurations but often underuse explicit motion dynamics that are crucial under appearance changes. We introduce a plug-and-play Wavelet Feature Stream that augments any skeleton backbone with time-frequency dynamics of joint velocities. Concretely, per-joint velocity sequences are transformed by the continuous wavelet transform (CWT) into multi-scale scalograms, from which a lightweight multi-scale CNN learns discriminative dynamic cues. The resulting descriptor is fused with the backbone representation for classification, requiring no changes to the backbone architecture or additional supervision. Across CASIA-B, the proposed stream delivers consistent gains on strong skeleton backbones (e.g., GaitMixer, GaitFormer, GaitGraph) and establishes a new skeleton-based state of the art when attached to GaitMixer. The improvements are especially pronounced under covariate shifts such as carrying bags (BG) and wearing coats (CL), highlighting the complementarity of explicit time-frequency modeling and standard spatio-temporal encoders.
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