Abstract
Gait recognition holds the promise of robustly identifying subjects based on their walking patterns instead of color information. While previous approaches have performed well for curated indoor scenes, they have significantly impeded applicability in unconstrained situations, e.g. outdoor, long distance scenes. We propose an end-to-end GAit DEtection and Recognition (GADER) algorithm for human authentication in challenging outdoor scenarios. Specifically, GADER leverages a Double Helical Signature to detect the fragment of human movement and incorporates a novel gait recognition method, which learns representations by distilling from an auxiliary RGB recognition model. At inference time, GADER only uses the silhouette modality but benefits from a more robust representation. Extensive experiments on indoor and outdoor datasets demonstrate that the proposed method outperforms the State-of-The-Arts for gait recognition and verification, with a significant 20.6% improvement on unconstrained, long distance scenes.
Abstract (translated)
步态识别的潜力是通过其行走模式而不是颜色信息,以 robustly 识别研究对象。尽管先前的方法在 curated 室内场景方面表现良好,但它们在无约束情况(如室外、远距离场景)的适用性方面却 significantly 限制了作用。我们提出一种 end-to-end GAit DEtection and Recognition (GADER) 算法,用于在挑战性的室外场景下对人类身份验证。具体来说,GADER 利用双曲签名来检测人类运动片段并纳入一种新的步态识别方法,该方法通过从辅助 RGB 识别模型中提取表示来学习表示。在推理时,GADER 仅使用轮廓模式,但得益于更稳健的表示。在室内和室外数据集上的广泛实验表明, proposed 方法在步态识别和验证方面优于最先进的方法,在无约束的远距离场景上实现了显著的 20.6% 改进。
URL
https://arxiv.org/abs/2307.14578
