解决遮挡下步态识别难题，无需依赖特定模型
MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation
- 用相关性知识蒸馏捕捉遮挡步态的序列内与序列间关联
- 在GREW、Gait3D等真实数据集上显著提升识别准确率
- 适用于各类步态识别模型，可无缝集成到现有系统
步态识别是一种远距离重要的生物特征技术。当前最先进的步态识别系统在近距离受控环境下表现优异，但近年来野外场景下的研究兴趣上升，得益于包含光照、视角和距离变化的复杂数据集。其中遮挡问题尤为突出——目标部分被摄像头视野遮挡。尽管重要，该问题却鲜有研究。为此，我们提出MimicGait，一种模型无关的遮挡步态识别方法。通过多实例相关性蒸馏损失训练网络，捕获同一主体在遮挡情况下的序列内与序列间相关性，并利用辅助可见性估计网络指导拟合网络的训练。我们在多个具有挑战性的现实数据集（如GREW、Gait3D和BRIAR）上验证了该方法的有效性。代码已开源：https://github.com/Ayush-00/mimicgait。
原文摘要 · Abstract (English)
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.
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