Retracted: Adaptive Recognition Method for VR Image of Wushu Decomposition Based on Feature Extraction

Abstract

At present, with the vigorous development of sports and the internationalization process of Wushu competition, the competition rules are constantly improving, and the Wushu routine competition is developing towards the direction of fast action combination and action difficulty. The existence and development of Competitive Wushu. Wushu routine is not only different from other competition quality regulations, but also has its unique performance. In this case, how to effectively measure the conventional level and the technical level of athletes has become the main problem to be solved in the sports field. On the basis of domestic and foreign research, this paper systematically studies the modeling and online updating methods of 2D PCA, and applies the 2D PCA algorithm to the foreground partition of overlapped blocks. Then, the research methods of background difference, optical flow and frame difference are used to establish the research model of adaptive recognition method of martial Arts decomposition action VR image based on feature extraction The results show that the extraction accuracy of the improved method is improved by 30%, and the action accuracy is higher, which has certain practical value.

Feature Extraction Method of Snowboard Starting Action Using Vision Sensor Image Processing

“…According to Figure 3, under multiple complex images, the recognition rates obtained by the feature extraction method in literatures [8]- [12] are low, so it can be verified that the action extraction effects of these four methods are poor, and the application effect is not as good as the method studied. e method to extract features of the starting action of snowboard in this method is good, and the recognition rate is more than 90%.…”

“…In the first experiment, by specifying the snowboard action image, the edge features of the image are extracted and the effect of edge feature extraction is compared; the second experiment mainly verifies the effect of snowboard action extraction under multiple complex images. In order to make the experimental results more illustrative, the methods of literatures [8], [9], [10], [11], and [12] are compared with the proposed methods. e experimental results are calculated by formula (5), and the experimental results are shown in Figure 3.…”

“…According to Figure 4, the feature extraction stability of the method in literature [8] is 0.41, the feature extraction stability of the method in literature [9] is 0.43, the feature extraction stability of the method in literature [10] is 0.47, the feature extraction stability of the method in literature [11] is 0.38, and the feature extraction stability of the method in literature [12] is 0.32, and the stability of feature extraction of the proposed method is 0.49. It can be seen that the snowboard action feature extraction using the method in this paper has better stability and practicability.…”

“…According to Figure 7, the feature extraction time of literature [8] method is 59, the feature extraction time of literature [9] method is 65, the feature extraction time of literature [10] method is 80, the feature extraction time of literature [11] method is 108, the feature extraction time of literature [12] method is 110, and the feature extraction time of the proposed method is 33. erefore, the snowboarding action feature extraction using this method takes less time. The proposed method Literature [9]method Literature [11]method…”

“…ere are many existing methods to extract the starting action features of snowboard [6,7]. Literature [8] proposed a human action image recognition method under high-intensity motion.…”