Motion Retrieval Based on Dynamic Bayesian Network and Canonical Time Warping

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.