At present, the existing gait recognition systems are focusing on developing methods to extract robust gait feature from silhouette images and they indeed achieved great success. However, gait can be sensitive to appearance features such as clothing and carried items. Compared with appearance-based method, model-based gait recognition is promising due to the robustness against these variations. In recent years, with the development of human pose estimation, the difficulty of model-based gait recognition methods has been mitigated. In this paper, to resist the increase of subjects and views variation, local features are built and a siamese network is proposed to maximize the distance of samples from the same subject. We leverage recent advances in action recognition to embed human pose sequence to a vector and introduce Spatial-Temporal Graph Convolution Blocks (STGCB) which has been commonly used in action recognition for gait recognition. Experiments on the very large population dataset named OUMVLP-Pose and the popular dataset, CASIA-B, show that our method archives some state-of-the-art (SOTA) performances in model-based gait recognition. The code and models of our method are available at https://github.com/timelessnaive/Gait-for-Large-Dataset after being accepted.
翻译:目前,现有的单步识别系统正在侧重于开发方法,从双影图像中提取稳健的步态特征,并确实取得了巨大成功;然而,对服装和随身携带的物品等外貌特征,动作可能十分敏感;与基于外观的方法相比,基于模型的步态识别很有希望,因为与这些差异相比,这种方法具有很强的活力;近年来,随着人造面估计的发展,基于模型的步态识别方法的难度已经减轻;在本文件中,为了抵制主题和观点差异的增加,建立了地方特征,并提议了一个赛马斯网络,以最大限度地扩大同一主题样本的距离;我们利用最近在行动识别方面的进展,将人姿势序列嵌入向矢量,并引入了空间-时动动区块(STGCB),这通常用于行动认知。关于名为UMVLP-Pose和大众数据集CASIA-B, 实验显示,我们的方法将一些州-艺术(SOATA)状态的性表现保存在基于模型的阵列式识别中。