Title
Human action recognition using multi-layer codebooks of key poses and atomic motions.
Abstract
Taking fully into consideration the fact that one human action can be intuitively considered as a sequence of key poses and atomic motions in a particular order, a human action recognition method using multi-layer codebooks of key poses and atomic motions is proposed in this paper. Inspired by the dynamics models of human joints, normalized relative orientations are computed as features for each limb of human body. In order to extract key poses and atomic motions precisely, feature sequences are segmented into pose feature segments and motion feature segments dynamically, based on the potential differences of feature sequences. Multi-layer codebooks of each human action are constructed with the key poses extracted from pose feature segments and the atomic motions extracted from motion feature segments associated with each two key poses. The multi-layer codebooks represent action patterns of each human action, which can be used to recognize human actions with the proposed pattern-matching method. Three classification methods are employed for action recognition based on the multi-layer codebooks. Two public action datasets, i.e., CAD-60 and MSRC-12 datasets, are used to demonstrate the advantages of the proposed method. The experimental results show that the proposed method can obtain a comparable or better performance compared with the state-of-the-art methods. Human actions are modeled by a sequence of key poses and atomic motions.Normalized relative orientations are computed as features for each limb.Feature sequences are segmented into pose and motion feature segments dynamically.Multi-layer codebooks which constructed with extracted key poses and atomic motions.A pattern-matching method is proposed and integrated with traditional classifiers.
Year
DOI
Venue
2016
10.1016/j.image.2016.01.003
Sig. Proc.: Image Comm.
Keywords
Field
DocType
Human action recognition,Key pose,Atomic motion,Multi-layer codebook
Computer vision,Normalization (statistics),Multi layer,Pattern recognition,Computer science,Action recognition,Artificial intelligence
Journal
Volume
Issue
ISSN
42
C
0923-5965
Citations 
PageRank 
References 
9
0.44
38
Authors
4
Name
Order
Citations
PageRank
Zhu, G.18312.50
Liang Zhang2578.47
P. Shen3394.17
Juan Song4808.45