标题： MimicGait：一种使用相关知识蒸馏的遮挡步态识别模型无关方法 显示英文标题

标题： MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation

摘要： 步态识别是一种在远距离下重要的生物特征技术。 最先进的步态识别系统在近距离的受控环境中表现非常好。 最近，由于收集了包含光照、俯仰角度和距离变化等更具有挑战性的户外数据集，引发了对野外步态识别的更多兴趣。 在这些环境中一个重要的问题是遮挡，即目标部分被摄像头视角阻挡。 尽管重要，但这个问题受到的关注很少。 因此，我们提出 MimicGait，一种在存在遮挡情况下的步态识别模型无关方法。 我们使用多实例相关蒸馏损失来训练网络，以捕捉受遮挡步态模式中序列间的和序列内的相关性，利用一个辅助 可见性估计网络来指导所提出的模仿网络的训练。 我们在像GREW、Gait3D和BRIAR这样的具有挑战性的现实世界数据集上展示了我们方法的有效性。 我们将代码发布在https://github.com/Ayush-00/mimicgait。 显示英文摘要

摘要： Gait recognition is an important biometric technique over large distances. State-of-the-art gait recognition systems perform very well in controlled environments at close range. Recently, there has been an increased interest in gait recognition in the wild prompted by the collection of outdoor, more challenging datasets containing variations in terms of illumination, pitch angles, and distances. An important problem in these environments is that of occlusion, where the subject is partially blocked from camera view. While important, this problem has received little attention. Thus, we propose MimicGait, a model-agnostic approach for gait recognition in the presence of occlusions. We train the network using a multi-instance correlational distillation loss to capture both inter-sequence and intra-sequence correlations in the occluded gait patterns of a subject, utilizing an auxiliary Visibility Estimation Network to guide the training of the proposed mimic network. We demonstrate the effectiveness of our approach on challenging real-world datasets like GREW, Gait3D and BRIAR. We release the code in https://github.com/Ayush-00/mimicgait.