Motion retrieval based on Dynamic Bayesian Network and Canonical Time Warping

被引：0
|
作者：
Xiao, Qinkun [1 ]
Yuan, Wei [1 ]
机构：
[1] Xian Technol Univ, Dept Elect Informat Engn, Xian, Peoples R China
来源：
2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1 | 2015年
关键词：
Motion retrieval; ACA; DBN; CTW matching;
D O I：
10.1109/ISCID.2015.164
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：60 / 63
页数：4
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