Abstract
Gait is becoming popular as a method of person re-identification because of its ability to identify people at a distance. However, most current works in gait recognition do not address the practical problem of occlusions. Among those which do, some require paired tuples of occluded and holistic sequences, which are impractical to collect in the real world. Further, these approaches work on occlusions but fail to retain performance on holistic inputs. To address these challenges, we propose RG-Gait, a method for residual correction for occluded gait recognition with holistic retention. We model the problem as a residual learning task, conceptualizing the occluded gait signature as a residual deviation from the holistic gait representation. Our proposed network adaptively integrates the learned residual, significantly improving performance on occluded gait sequences without compromising the holistic recognition accuracy. We evaluate our approach on the challenging Gait3D, GREW and BRIAR datasets and show that learning the residual can be an effective technique to tackle occluded gait recognition with holistic retention.
Abstract (translated)
步态识别作为一种重新识别人员的方法正因其能够在远距离识别人的能力而变得越来越流行。然而，目前大多数关于步态识别的工作并没有解决实际中的遮挡问题。在少数研究遮挡问题的现有工作中，一些方法需要成对的遮挡序列和完整序列数据，这种需求在现实世界中难以获取。此外，这些方法虽然能处理遮挡情况，但在面对无遮挡的情况时性能表现不佳。 为了解决这些问题，我们提出了RG-Gait（残差矫正步态识别）这一方法，旨在解决遮挡下的步态识别问题，并保持对完整序列的识别准确性。我们将该问题视为一个残差学习任务，将被遮挡的步态特征看作是从完整的步态表示中偏离的一个残差。我们的网络能够自适应地整合所学得的残差信息，在不牺牲完全视图下步态识别准确性的前提下，显著提高对部分遮挡序列的识别性能。 我们在具有挑战性的Gait3D、GREW和BRIAR数据集上评估了我们提出的方法，并展示了学习残差可以作为一种有效的技术来处理带有完整视图保留能力的被遮挡步态识别问题。
URL
https://arxiv.org/abs/2507.10978
