Abstract
Gait, the manner of walking, has been proven to be a reliable biometric with uses in surveillance, marketing and security. A promising new direction for the field is training gait recognition systems without explicit human annotations, through self-supervised learning approaches. Such methods are heavily reliant on strong augmentations for the same walking sequence to induce more data variability and to simulate additional walking variations. Current data augmentation schemes are heuristic and cannot provide the necessary data variation as they are only able to provide simple temporal and spatial distortions. In this work, we propose GaitMorph, a novel method to modify the walking variation for an input gait sequence. Our method entails the training of a high-compression model for gait skeleton sequences that leverages unlabelled data to construct a discrete and interpretable latent space, which preserves identity-related features. Furthermore, we propose a method based on optimal transport theory to learn latent transport maps on the discrete codebook that morph gait sequences between variations. We perform extensive experiments and show that our method is suitable to synthesize additional views for an input sequence.
Abstract (translated)
步态(Gait)是步行的方式,已被证明是一种可靠的生物特征,可用于监视、营销和安保等领域。一个有前途的新方向是通过自监督学习方法训练输入步态序列的步态识别系统。这些方法 heavily reliant on strong augmentations for the same walking sequence to induce more data variability and to simulate additional walking variations. 当前的数据增强方案是启发式的,无法提供必要的数据变化,因为它们只能提供简单的时间和空间扭曲。在本研究中,我们提出了步态变形方法(Gait Morph),一种修改输入步态序列的新方法。我们的方法涉及训练一个高压缩的步态骨骼序列模型,利用未标记数据建立离散且可解释的潜在空间,以维持身份相关特征。此外,我们提出了基于最优传输理论的方法,学习离散代码book中的过渡传输映射,以morph步态序列之间的变化。我们进行了广泛的实验,并表明我们的方法适合合成输入序列的额外视角。
URL
https://arxiv.org/abs/2307.14713
