Yuwei Wang

This paper presents an in-depth analysis of tennis match scene classification using an adaptive Gaussian mixture model parameter estimation simulation algorithm. We divided the main components of semantic analysis into type of motion, distance of motion, speed of motion, and landing area of the tennis ball. Firstly, for the problem that both people and tennis balls in the video frames of tennis matches from the surveillance viewpoint are very small, we propose an adaptive Gaussian mixture model parameter estimation algorithm, which has good accuracy and speed on small targets. Secondly, in this paper, we design a sports player tracking algorithm based on role division and continuously lock the target player to be tracked and output the player region. At the same time, based on the displacement information of the key points of the player’s body and the system running time, the distance and speed of the player’s movement are obtained. Then, for the problem that tennis balls are small and difficult to capture in high-speed motion, this paper designs a prior knowledge-based algorithm for predicting tennis ball motion and landing area to derive the landing area of tennis balls. Finally, this paper implements a prototype system for semantic analysis of real-time video of tennis matches and tests and analyzes the performance indexes of the system, and the results show that the system has good performance in real-time, accuracy, and stability.

The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as the primary data source. The unsupervised word segmentation methods are used to detect Out-of-Vocabulary, and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF-IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means-Ends” two-dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale-free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization.