Research on Evaluation Indicator of Ice Rink and Curling Stone Motion for the 2022 Beijing Winter Olympic Games Based on Video Recognition Method

Abstract

During curling sports, the movement of the stone is affected by the quality of the ice. Therefore, the delivery team led by the ice maker hopes that the quality of the ice surface will be stable and that the athletes will always ‘read the ice’ and pay attention to the small changes in the ice surface. This phenomenon is the charm of curling. Many friction models have been proposed to describe the regularity of the curling motion. In the curling competitions of the 2022 Beijing Winter Olympic Games, the 2021 World Wheelchair Curling Championships, and the warm-up competition before, the research team installed a video image capture system in the arena to capture and record the data of the curling motion by using the depth neural network and object tracking algorithm. Further motion data research verifies the relationship between the friction coefficient and the speed. The quality control parameter of ice rink α is proposed, which is related to the influencing factors of the ice surface temperature, the ice hardness, the size of the pebble point, and the width of the curling friction band. The quality of the curling ice rink can be evaluated accurately and comprehensively by using parameter α. Based on the relationship between the friction coefficient and the speed, a physical model of horizontal sliding of the curling stone is established, which agrees well with the results of data obtained from video acquisition. Therefore, the movement distance along the rink can be accurately predicted. This paper analyzes the relationship between the long-time (the time it takes for the curling stone to travel between the two hog lines) and the stop position and that between the long-time and the split-time (the time it takes for the curling stone to travel from the back line to the hog line). Based on this result, a ruler can be established to assist athletes in estimating the sliding distance of the stone before curling throwing. This research also studies the relationship between three factors (the sliding speed in the x-direction, the angular speed, and a tiny lateral deflection speed in the y-direction) and the deviation of the stone. At the same time, there are also some interesting phenomena of the lateral deflection of the stone, such as the relationship between the lateral deflection angle tanθ and the initial lateral speed. As a result, the prediction of the curling stone’s exact final location can be realized. In summary, this article proposes an indicator for evaluating the quality of ice rinks and a physical model of curling based on the curling friction model, which is validated by data obtained from a video capture system of the 2022 Beijing Winter Olympics. The results described above have been applied in the post-match operation of the National Aquatics Center to guide the production of Olympic-grade ice surfaces and to guide athletes to “read ice” accurately during training.