Pattern Recognition of Wushu Routine Action Decomposition Process Based on Kinect

Chenxing Cao, Bai Shan, Haiyan Zhang and Baiyuan Ding

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Human action recognition is a hotspot in the fields of computer vision and pattern recognition. Human action recognition technology has created huge social value and considerable economic value for the society. Meeting people’s needs and understanding people’s expressions are the current research focus. Aiming at the problem that the movement cannot be continuously identified and due to a lack of detailed features in the action decomposition pattern recognition in the traditional Wushu routine decomposition process, it is proposed to use Kinect technology to identify the Wushu routine movement decomposition process in the Wushu routine movement decomposition process. This paper analyzes the principle of skeleton tracking and skeleton extraction performed by the Kinect human sensor and uses the Kinect sensor with the Visual Studio 2015 development platform to collect and process the skeleton data of limb movements and defines eight static limb motion samples and four dynamic limbs. The study uses a deep learning neural network algorithm to train and identify the established database of static body movements and uses the same template matching algorithm and K-NN. The recognition effects of the algorithms were compared and analyzed, and it was concluded that the static body motion recognition rates of the three algorithms were all above 90%. In this paper, recognition experiments are carried out on the MSR action 3D database. The influence of different integrated decision-making methods on the recognition results is further discussed and analyzed, and the average method integrated decision-making, which is most suitable for the algorithm model in this paper, is proposed. The results show that the recognition accuracy of the algorithm reaches 98.1%, which proves the feasibility of the preprocessing algorithm.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3876487

DOI: 10.1155/2022/3876487

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