Abstract
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.
Abstract (translated)
鲁棒的步态识别需要高度区分性的表示，这与输入模态密切相关。虽然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捉足够的可用于处理视角变化的信息，并且也无法捕获步态中的细微而有意义的细节。为此，在本文中我们引入了一个新的框架，称为DepthGait，该框架结合了从RGB图像序列派生出的深度图和轮廓图，以增强步态识别能力。 具体来说，除了人类身体的传统二维轮廓表示之外，所提出的管道还能够直接从给定的RGB图像序列中估计深度图，并将其用作一种新的模态来捕获人体运动中的区分性特征。此外，我们还开发了一种新颖的多尺度和跨级融合方案，以弥合深度图与轮廓图之间的模式差距。 在标准基准测试上的广泛实验表明，提出的DepthGait框架相较于同类方法达到了最先进的性能，并且在一个具有挑战性的数据集上实现了令人印象深刻的平均准确率。
URL
https://arxiv.org/abs/2508.03397
