DepthGait：基于RGB衍生深度与轮廓序列的多尺度跨层级特征融合用于鲁棒步态识别
DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
AI总结：
本文提出DepthGait框架，融合RGB衍生深度图和轮廓，通过多尺度跨层级融合方案增强步态识别，在标准基准上达到最先进性能。
摘要：
鲁棒的步态识别需要高度判别性的表示，这与输入模态密切相关。尽管二值轮廓和骨架在近期文献中占主导地位，但这些二维表示不足以捕获足够的线索来处理视角变化，并捕捉步态的更精细和有意义的细节。在本文中，我们引入了一个新颖的框架，称为DepthGait，该框架融合了RGB衍生的深度图和轮廓以增强步态识别。具体而言，除了人体的二维轮廓表示外，所提出的流程从给定的RGB图像序列中显式估计深度图，并将其作为一种新模态来捕获人类运动固有的判别性特征。此外，还开发了一种新颖的多尺度和跨层级融合方案，以弥合深度图与轮廓之间的模态差距。在标准基准上的大量实验表明，所提出的DepthGait相比同类方法实现了最先进的性能，并在具有挑战性的数据集上达到了令人印象深刻的平均rank-1准确率。
英文摘要：
Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait recognition. Specifically, apart from the 2D silhouette representation of the human body, the proposed pipeline explicitly estimates depth maps from a given RGB image sequence and uses them as a new modality to capture discriminative features inherent in human locomotion. In addition, a novel multi-scale and cross-level fusion scheme has also been developed to bridge the modality gap between depth maps and silhouettes. Extensive experiments on standard benchmarks demonstrate that the proposed DepthGait achieves state-of-the-art performance compared to peer methods and attains an impressive mean rank-1 accuracy on the challenging datasets.
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