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| Human Action Recognition Using RGB-D Image Features |
| TANG Chao1, WANG Wenjian2, ZHANG Chen1, PENG Hua3, LI Wei4 |
1.School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601;
2.School of Computer and Information Technology, Shanxi University, Taiyuan 030006;
3.Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000;
4.School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024 |
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Abstract Since the existing multi-modal feature fusion methods cannot measure the contribution of different features effectively, a human action recognition method based on RGB-depth image features is proposed. Firstly, the histogram of oriented gradient feature based on RGB modal information, the space-time interest points feature based on depth modal information, and the joints relative position feature based on joints modal information are acquired to express human actions, respectively. Then, nearest neighbor classifiers with different distance measurement formulas are utilized to classify prediction samples expressed by the three modal features. The experimental results on public datasets show that the proposed method is simple, fast and efficient.
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Received: 15 June 2019
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| Fund:Supported by National Natural Science Foundation of China(No.61673249,61806068,61662025), Excellent Talents Training Project of Universities of Anhui Province(No.gxfx2017099), Scholarship for Studying Abroad Program of Fujian, Science and Technology Planning Guidance Project of Xiamen(No.3502Z20179038), Key Teaching and Research Project of Hefei University(No.2018 hfjyxm09) |
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Corresponding Authors:
TANG Chao, Ph.D., associate professor. His research interests include machine learning and compu-ter vision.
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| About author:: WANG Wenjian, Ph.D., professor. Her research interests include machine learning and computer intelligence;ZHANG Chen, Ph.D., lecturer. Her research interests include machine learning and computer intelligence;PENG Hua, Ph.D., lecturer. His research interests include brain-like intelligent systems, human-robot interaction and machine learning;LI Wei, Ph.D., associate professor. His research interests include artificial Intelligence, computer graphics and human compu-ter interaction. |
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2019, Vol. 32 
