Human gait is one of important biometric characteristics for human identification at a distance. In practice, occlusion usually occurs and seriously affects accuracy of gait recognition. However, there is no available database to support in-depth research of this problem, and state-of-arts gait recognition methods have not paid enough attention to it, thus this paper focuses on gait recognition under occlusion. We collect a new gait recognition database called OG RGB+D database, which breaks through the limitation of other gait databases and includes multimodal gait data of various occlusions (self-occlusion, active occlusion, and passive occlusion) by our multiple synchronous Azure Kinect DK sensors data acquisition system (multi-Kinect SDAS) that can be also applied in security situations. Because Azure Kinect DK can simultaneously collect multimodal data to support different types of gait recognition algorithms, especially enables us to effectively obtain camera-centric multi-person 3D poses, and multi-view is better to deal with occlusion than single-view. In particular, the OG RGB+D database provides accurate silhouettes and the optimized human 3D joints data (OJ) by fusing data collected by multi-Kinects which are more accurate in human pose representation under occlusion. We also use the OJ data to train an advanced 3D multi-person pose estimation model to improve its accuracy of pose estimation under occlusion for universality. Besides, as human pose is less sensitive to occlusion than human appearance, we propose a novel gait recognition method SkeletonGait based on human dual skeleton model using a framework of siamese spatio-temporal graph convolutional networks (siamese ST-GCN). The evaluation results demonstrate that SkeletonGait has competitive performance compared with state-of-art gait recognition methods on OG RGB+D database and popular CAISA-B database.

翻译：人类行踪是人类远程识别的重要生物鉴别特征之一。 在实践中, 隐蔽通常会发生, 并严重影响动作识别的准确性。 但是, 没有可用的数据库支持对这一问题的深入研究, 并且艺术状态识别方法没有足够关注这一问题, 因此本文侧重于在隐蔽下对动作识别的注意。 我们收集了一个名为 OG RGB+D 的新动作识别数据库, 它打破了其他动作数据库的局限性, 包括各种敏感行迹( 自我隐蔽、 主动隐蔽、 被动隐蔽) 的多重隐蔽性视频数据。 特别是, 我们多个同步的 Azect DK 传感器数据采集系统( Multi Kinect SDAS) 没有足够关注这一问题, 因此, 由于 Azure Kinect DK 可以同时收集多式数据支持不同种类的 Gait 识别算法, 特别是使我们能够有效地获得以相机为中心的多人3D 构成的多视角, 多视角比单一视角更能处理我们隐蔽的隐蔽性数据。 特别是, RGB+KD 数据库在使用精确的服务器上, 将一个正在使用精确的Scial IMD 数据识别的 Ode 数据识别的 Ode 。