A Benchmark for Gait Recognition under Occlusion Collected by Multi-Kinect SDAS

Abstract

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. This database will be available at https://github.com/cvNXE/OG-RGB-D-gait-database.

Online hand position detection and classification system using multiple classification algorithms

“…To extract position skeleton data, Kinect provides to us the locations of 25 virtual anatomical joint trajectories which can be extracted from depth map with a per-pixel semantic segmentation algorithm [3], with the ability to track 6 people, the Kinect sensor provides a powerful software development kit (SDK). Its technology allowed many applications to be developed beyond the original scope of gaming, covering several categories like detection of the human body or a part of it, such as the face, hands, or legs, and distinguishing movements and gestures in the field of sign language, gait recognition as in research [4]- [9]. Also, to monitor patients and the elderly for healthcare or from falling and alert those concerned where one or several devices are used [10]- [12].…”