Short-time Human Activity Recognition Based on Wavelet Features Matching

Authors

Benyue Su, 1.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China;2.School of Mathematics and Computer, Tongling University, Tongling 244061, China;Follow
Li Zhang, 1.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China;2.School of Mathematics and Computer, Tongling University, Tongling 244061, China;3.School of Computer and Information, Anqing Normal University, Anqing 246133, China;
Qingxuan He, 1.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China;4.School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China;
Min Sheng, 1.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China;4.School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China;

Abstract

Abstract: The selection of features is the key problem in the study of human activity recognition. In order to obtain sufficient and stable behavioral features, long-time behavioral data that exceed one behavior cycle are often processed, while short-time behavioral data with less than one behavioral cycle are usually unstable, making it difficult to achieve accurate and stable identification. This paper proposes a short-time human activity recognition method based on the combination of wavelet transform and template matching. Coefficient features are extracted using wavelet transform method. The features of the short-time test samples are matched with the features in the template library to make classification recognition of activity based on similarity. The experimental results show that this method has more accurate and stable recognition performance for short-time behavioral activity, which helps to realize real-time recognition of human behavioral activity.

Recommended Citation

Su, Benyue; Zhang, Li; He, Qingxuan; and Sheng, Min (2023) "Short-time Human Activity Recognition Based on Wavelet Features Matching," Journal of System Simulation: Vol. 35: Iss. 1, Article 13.
DOI: 10.16182/j.issn1004731x.joss.22-0176
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss1/13

First Page

158

Revised Date

2022-06-03

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.22-0176

Last Page

168

CLC

TP391.4

Recommended Citation

Benyue Su, Li Zhang, Qingxuan He, Min Sheng. Short-time Human Activity Recognition Based on Wavelet Features Matching[J]. Journal of System Simulation, 2023, 35(1): 158-168.

DOI

10.16182/j.issn1004731x.joss.22-0176