Action Recognition Based on Depth Motion Map and Hybrid Classifier

Wenhui Li, Qiuling Wang and Ying Wang

Mathematical Problems in Engineering, 2018, vol. 2018, 1-10

Abstract:

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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2018/8780105.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2018/8780105.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8780105

DOI: 10.1155/2018/8780105

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().