Human Action Recognition Based on Multiple Feature Fusion

Human Action Recognition Based on Multiple Feature Fusion

R.J.Ma H.S. Zhang 

Department of Electronic and Information, Northwestern Polytechnical University China, 127 West Youyi RoadXi’an Shaanxi

Corresponding Author Email: 
1561000382@qq.com; zhanghuisheng@nwpu.edu.cn
Page: 
25-42
|
DOI: 
https://doi.org/10.18280/ama_b.600102
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

Human actions recognition generally uses geometric or statistical characteristics as training data. The geometric characteristics of the image and waveform display can be described by Pulse coupled neural network (PCNN). Experiential model decomposition (EMD) algorithm can be used for the feature extraction of waveforms. Therefore, we propose a motion feature description algorithm combined with PCNN and EMD. The experimental results show that using the method of PCNN-EMD-based feature recognition can obtain a high accuracy rate, while using the fusion feature of KPCA has a higher recognition rate.

Keywords: 

human action recognition, EMD, gabor, PCNN, KPCA

1. Introduction
2. Image Preprocessing
3. Action Image Feature Extraction by Texture
4 Statistical Features
5. Experiment Analysis
6. Conclusion
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