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Real-time human action recognition using depth motion maps and convolutional neural networks

Authors :
Li, Jiang
Ban, Xiaojuan
Yang, Guang
Li, Yitong
Wang, Yu
Source :
International Journal of High Performance Computing and Networking; 2019, Vol. 13 Issue: 3 p312-320, 9p
Publication Year :
2019

Abstract

This paper presents an effective approach for recognising human actions from depth video sequences by employing depth motion maps (DMMs) and convolutional neural networks (CNNs). Depth maps are projected onto three orthogonal planes, and frame differences under each view (front/side/top) are then accumulated through an entire depth video sequence generating a DMM. We build a model architecture of multi-view convolutional neural network (MV-CNN) containing multiple networks to deal with three DMMs (DMMf, DMMs, DMMt). The output of full-connected layer under each view is integrated as feature representation, which is then learned in the last softmax regression layer to predict human actions. Experimental results on MSR-Action3D dataset and UTD-MHAD dataset indicate that the proposed approach achieves state-of-the-art recognition performance and is appropriate for real-time recognition.

Details

Language :
English
ISSN :
17400562 and 17400570
Volume :
13
Issue :
3
Database :
Supplemental Index
Journal :
International Journal of High Performance Computing and Networking
Publication Type :
Periodical
Accession number :
ejs49677437
Full Text :
https://doi.org/10.1504/IJHPCN.2019.098572