Experimental Measurement of Ice-Curling Stone Friction Coefficient Based on Computer Vision Technology: A Case Study of “Ice Cube” for 2022 Beijing Winter Olympics

Abstract

In the curling sport, the coefficient of friction between the curling stone and pebbled ice is crucial to predict the motion trajectory. However, the theoretical and experimental investigations on stone–ice friction are limited, mainly due to the limitations of the field measurement techniques and the inadequacy of the experimental data from professional curling rinks. In this paper, on-site measurement of the stone–ice friction coefficient in a prefabricated ice rink for the Beijing Winter Olympics curling event was carried out based on computer vision technology. Firstly, a procedure to determine the location of the curling stone was proposed using YOLO-V3 (You Only Look Once, Version 3) deep neural networks and the CSRT Object tracking algorithm. Video data was recorded during the curling stone throwing experiments, and the friction coefficient was extracted. Furthermore, the influence of the sliding velocity on the friction coefficient was discussed. Comparison with published experimental data and models and verification of the obtained results, using a sensor-based method, were conducted. Results show that the coefficient of friction (ranging from 0.006 to 0.016) decreased with increasing sliding velocity, due to the presence of a liquid-like layer. Our obtained results were consistent with the literature data and the friction model of Lozowski. In addition, the experimental results of the computer vision technique method and the accelerometer sensor method showed remarkable agreement, supporting the accuracy and reliability of our proposed measurement procedure based on deep learning.

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

“…The friction coefficient, belonging to a mixed lubrication regime, showed descent as the curling stone sliding velocity increased. This is consistent with the law of the effect of speed on the friction coefficient as described by Li [28], indicating the reliability of the measurement method based on artificial intelligence.…”

“…Therefore, the relationship between tanθ (the tangent of the lateral deflection angle, that is, the ratio of the y-direction lateral deflection of the curling stone to the x-direction sliding distance) and the initial speed in the y-direction vy is analyzed to assist athletes in determining the final stop position. The relationship, similar to the power function, can be expressed as Equation (28). As shown in Figure 19, there is a strong correlation between the direction of rotation and the direction of the deviation.…”

“…He found that swinging around the discrete left-right asymmetric friction points was the dominant curling mechanism. Li et al [28] measured the stone-ice friction coefficient on-site in a prefabricated ice rink based on computer vision technology. However, there are few studies of curling using computer vision technology, so the dataset is relatively small.…”

“…determining the final stop position. The relationship, similar to the power function, can be expressed as Equation (28).…”

The Split Friction Model - The Isotropic Origin of the Lateral Force in Curling

“…Most recently, Murata [22] experimentally favored left-right asymmetry while Ohashi [23] focused on a front-back asymmetry due to strain-hardening of the ice close to melting temperature. Furthermore, Li et al [24] provided measurements of µ, using a high-precision camera system and a deep neural network algorithm.…”