Wushu Movement Recognition System Based on DTW Attitude Matching Algorithm

• Published in: Entertainment computing Vol. 52; p. 100877
• Main Authors: Wu, Guosong, Wen, Chunhong, Jiang, Hecai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2025
• Subjects: Action recognition; Dynamic time warping; Pose matching; Video frame; Wushu; Action recognition; Wushu; Pose matching; Dynamic time warping; Video frame
• ISSN: 1875-9521
• DOI: 10.1016/j.entcom.2024.100877

•A hierarchical model was constructed and the K-means algorithm was applied to optimize the tree kernel type, and the maximum-minimum dynamic time warping model was proposed.•The model achieves a recognition rate of 95.2% in the KTH dataset.•The average recognition rate of the proposed method is 98.8% and the training time of the model is 184 seconds.•The method used in this study has good recognition performance and is feasible in practice. Motion recognition technology is widely used in intelligent video surveillance, human-computer interaction and other fields. With the development of computer vision technology, improving the accuracy and efficiency of motion recognition has become the focus of research. The purpose of this study is to improve the performance of Wushu movement recognition through improved dynamic time warping algorithm and hierarchical model. Firstly, a high-dimensional feature vector is constructed by using the position, velocity and Angle changes of human bone joints. The actions are subdivided by the hierarchical model, and matched and recognized by the max-minimum dynamic time regularization model. Meanwhile, the K-class mean algorithm is combined to optimize the type of tree core, improve the performance of the model, reduce the interference of noise nodes, and effectively classify Wushu actions. Experimental verification was carried out on four public data sets of KTH, Olympic Sports, Hollywood2 and HMDB51. The experimental results showed that the recognition rate of the proposed model in KTH data set was 95.2%, and that in Olympic Sports data set was 91.4%. The Hollywood2 dataset was 66.7%, and the HMDB51 dataset was 61.2%. Comparing the results of different algorithms, the proposed method improved the recognition performance by 10% compared with long short-term memory network and gated cycle unit. Compared with one-dimensional convolutional neural network, the time of the proposed method was 15s longer, but the recognition rate was 1.6% higher. The results showed that the proposed method had significant performance advantages in diverse and complex action recognition tasks. Meanwhile, the results emphasized the factors to be considered in the design of the model, demonstrating its effectiveness in the application of Wushu movement recognition.