Abstract
Recently, Human action recognition based on 3D Skeleton sequences has gained significant interest in the computer vision field. However, skeleton sequences are sensitive to noises, viewpoint variations and similar movements. To sort out these problems, this paper proposes a View Invariant Spatio-Temporal Descriptor (VISTD) that encodes the positions of skeleton joints in each frame of an action sequence. VISTD is a combination of View Invariant Skeleton Joint Descriptor (VISJD) and Spatio-Temporal Skeleton Joint Descriptor (STSJD). Further, we employ a standard Convolutional Neural Network (CNN) architecture for classification. To analyze the impact of fusion methods, the results of CNN model are fused with different fusion rules. Extensive simulation experiments carried out over proposed model through UWA3DII dataset and NTU-RGB+D dataset. The comparison of experimental results with existing methods explores the superiority.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
