Prediction algorithm and simulation of tennis impact area based on semantic analysis of prior knowledge.

The performance of target detection algorithms for manual features is gradually saturated, and the development of target detection has stagnated. Computer vision is a discipline that studies how to use computers to replace the human eye and use visual information and visual algorithms to automatically detect, recognize, and track target objects. Target detection has also been fully developed as a basis for solving advanced vision tasks such as target tracking, instance segmentation, image understanding, and behavior recognition. The theories of artificial intelligence and deep learning has gradually developed, making convolutional neural networks a hot spot of attention in the field of computer vision and image processing. Based on the work in the field of target detection, this paper proposes a target detection algorithm based on the prior knowledge of the impact area of the tennis ball. Our experiments and evaluations show that the algorithm can achieve higher detection accuracy and faster detection speed. Moreover, the proposed algorithm accurately tracks and judge the impact area of tennis sports, and improve the accuracy and reliability of the tennis impact. [ABSTRACT FROM AUTHOR]