Open AccessAction Recognition Based on Depth Motion Map and Hybrid Classifier
Author(s) -
Wenhui Li,
Qiuling Wang,
Ying Wang
Publication year - 2018
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2018/8780105
Subject(s) - artificial intelligence , discriminative model , encode , pattern recognition (psychology) , computer science , robustness (evolution) , depth map , classifier (uml) , computer vision , action recognition , feature extraction , extreme learning machine , image (mathematics) , artificial neural network , biochemistry , chemistry , gene , class (philosophy)
In order to efficiently extract and encode 3D information of human action from depth images, we present a feature extraction and recognition method based on depth video sequences. First, depth images are projected continuously onto three planes of Cartesian coordinate system, and differential images of the respective projection surfaces are accumulated to obtain the complete 3D information of the depth motion maps (DMMs). Then, discriminative completed LBP (disCLBP) encodes depth motion maps to extract effective human action information. A hybrid classifier combined with Extreme Learning Machine (ELM) and collaborative representation classification (CRC) is employed to reduce the computational complexity while reducing the impact of noise. The proposed method is tested on the MSR-Action3D database; the experimental results show that it achieves 96.0% accuracy and well performs better robustness comparing to other popular approaches.