NDLI: Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps

Content Provider	IEEE Xplore Digital Library
Author	Jiajia Luo Wei Wang Hairong Qi
Copyright Year	2013
Description	Author affiliation: Univ. of Tennessee, Knoxville, TN, USA (Jiajia Luo; Wei Wang; Hairong Qi)
Abstract	Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free styles in performing actions, may cause large intra-class variations. In this paper, a new framework based on sparse coding and temporal pyramid matching (TPM) is proposed for depth-based human action recognition. Especially, a discriminative class-specific dictionary learning algorithm is proposed for sparse coding. By adding the group sparsity and geometry constraints, features can be well reconstructed by the sub-dictionary belonging to the same class, and the geometry relationships among features are also kept in the calculated coefficients. The proposed approach is evaluated on two benchmark datasets captured by depth cameras. Experimental results show that the proposed algorithm repeatedly achieves superior performance to the state of the art algorithms. Moreover, the proposed dictionary learning method also outperforms classic dictionary learning approaches.
Sponsorship	IEEE Comput. Soc.
Starting Page	1809
Ending Page	1816
File Size	389644
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.227
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Joints Encoding Three-dimensional displays Geometry Feature extraction Vectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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