Abstract
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 this https URL.
Abstract (translated)
步态识别是一种重要的生物识别技术，尤其适用于远距离的应用场景。目前最先进的步态识别系统在受控环境下的近距离表现非常出色。最近，由于收集到了包含光照变化、仰角变化和距离差异等挑战的户外数据集，人们对野外条件下的步态识别产生了越来越多的兴趣。这些环境中一个重要且较少受到关注的问题是遮挡问题，即目标对象部分被摄像机视野阻挡的情况。 为了解决这一问题，我们提出了MimicGait方法，这是一种模型无关的方法，用于在存在遮挡的情况下进行步态识别。通过使用多实例相关蒸馏损失函数来训练网络，该方法能够捕捉到一个主体的被遮挡步态模式之间的序列内外相关性，并利用辅助的可见性估计网络来指导我们提出的模仿网络的学习过程。 我们在具有挑战性的现实世界数据集（如GREW、Gait3D和BRIAR）上展示了我们的方法的有效性。我们的代码可在上述链接中获取。
URL
https://arxiv.org/abs/2501.15666
