Abstract
A novel graph-based motion retrieval method is proposed. The method includes the 2 main stages: (1) in stage of learning, firstly, for each of motion in database, using Aligned Cluster Analysis (ACA) to get key frames, extracting body gesture and joint state features as observation signal of graph model, based on graph model theory and statistical learning of key frame, a new Dynamic Bayesian Network (DBN) frame is constructed. The graph-based motion descriptor is built based on DBN inference, and graph-based motion feature database is constructed. (2) In stage of motion retrieval, we can recognize category of motion through Canonical Time Warping(CTW) matching results.
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.
