NDLI: Human action recognition on depth dataset

Content Provider	Springer Nature Link
Author	Gao, Zan Zhang, Hua Liu, Anan A. Xu, Guangping Xue, Yanbing
Copyright Year	2015
Abstract	Human action recognition is a hot research topic; however, the change in shapes, the high variability of appearances, dynamitic background, potential occlusions in different actions and the image limit of 2D sensor make it more difficult. To solve these problems, we pay more attention to the depth channel and the fusion of different features. Thus, we firstly extract different features for depth image sequence, and then, multi-feature mapping and dictionary learning model (MMDLM) is proposed to deeply discover the relationship between these different features, where two dictionaries and a feature mapping function are simultaneously learned. What is more, these dictionaries can fully characterize the structure information of different features, while the feature mapping function is a regularization term, which can reveal the intrinsic relationship between these two features. Large-scale experiments on two public depth datasets, MSRAction3D and DHA, show that the performances of these different depth features have a big difference, but they are complementary. Further, the features fusion by MMDLM is very efficient and effective on both datasets, which is comparable to the state-of-the-art methods.
Starting Page	2047
Ending Page	2054
Page Count	8
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	7
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-09-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Human action recognition Depth image Multi-feature Feature mapping MMDLM Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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