Research on Enhancement of Curling Training and Decision-making through Independent Component Analysis

Intelligent training and decision-making process in curling games are of a great significance to the fairness of the sports game. This paper investigates an intelligent method for image feature extraction and trajectory tracking using independent component analysis (ICA), with a focus on robust subspace learning techniques. The primary objective is to enhance the precision of trajectory tracking in high-standard curling games during training and decision-making processes. Addressing the challenges of handling noisy and diverse image data in real-world scenarios, the study meticulously examines both the theoretical foundations and practical implementation of the method. The proposed approach demonstrates the ability to extract significant movement trajectory features from the entire curling trajectory amid uncertainties. By employing robust subspace learning, the method aims to improve the performance of curling trajectory analysis tasks, including object recognition and scene understanding. Comprehensive evaluations of the intelligent trajectory feature extraction techniques highlight their effectiveness and versatility. The research outcomes contribute significantly to the advancement of computer vision systems, facilitating enhanced trajectory interpretation across various sports tracking applications, from surveillance to autonomous systems. Simulations conducted to test the feasibility of the proposed method underscore its potential impact and practical applicability.