Abstract
Gait recognition aims to identify a person based on their walking sequences, serving as a useful biometric modality as it can be observed from long distances without requiring cooperation from the subject. In representing a person's walking sequence, silhouettes and skeletons are the two primary modalities used. Silhouette sequences lack detailed part information when overlapping occurs between different body segments and are affected by carried objects and clothing. Skeletons, comprising joints and bones connecting the joints, provide more accurate part information for different segments; however, they are sensitive to occlusions and low-quality images, causing inconsistencies in frame-wise results within a sequence. In this paper, we explore the use of a two-stream representation of skeletons for gait recognition, alongside silhouettes. By fusing the combined data of silhouettes and skeletons, we refine the two-stream skeletons, joints, and bones through self-correction in graph convolution, along with cross-modal correction with temporal consistency from silhouettes. We demonstrate that with refined skeletons, the performance of the gait recognition model can achieve further improvement on public gait recognition datasets compared with state-of-the-art methods without extra annotations.
Abstract (translated)
翻译： 步态识别的目的是根据一个人的行走序列来识别这个人，作为一个有用的生物测量指标，因为它可以从很远的距离上通过合作观察到，而不需要对被测者进行合作。在表示一个人的行走序列时，轮廓和骨架是两种主要的模式。轮廓序列在多个身体部位之间发生重叠时缺乏详细的部分信息，并受到携带物品和服装的影响。骨架，由连接关节的关节和骨头组成，提供不同部位更准确的部分信息；然而，它们对遮挡和低质量图像敏感，导致序列中的帧结果不一致。在本文中，我们探讨了使用骨架的双流表示方法进行步态识别，同时使用轮廓。通过将轮廓和骨架的合并数据进行融合，我们通过自校正的图卷积和对时一致的跨模态校正在轮廓和骨架上进行优化。我们证明了，通过优化骨架，步态识别模型的性能可以在没有额外注释的公共步态识别数据集上实现比最先进方法更进一步的改进。
URL
https://arxiv.org/abs/2404.02345
