Abstract
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.
Highlights
With the Olympic Games, World Cup, and other large sports events, people are becoming increasingly obsessed with sports
With the improvement of national living standards, outbound tourism has shown a high growth trend in recent years, the residents’ consumption upgrade has promoted the transformation and upgrading of the tourism industry, and compared with other industries, tourism consumption is more inclined to experience-based consumption, tourism, and other industries to integrate the development of new business models to meet diversified consumer demand; “tourism” will become the new trend of the phase of the development of the outbound tourism industry
The traditional form of watching tennis matches has been unable to meet the needs of people’s sports entertainment, and with the advent of the era of artificial intelligence, the traditional form of tennis matches broadcast intelligent upgrade has given the time to wait for a wide audience of badminton naturally affected by the development of technology and the birth of some intelligent devices based on data analysis [1]
Summary
Received 17 April 2021; Revised 5 May 2021; Accepted 8 May 2021; Published 19 May 2021. Is 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. 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. 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
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