Computer Vision-Driven Evaluation System for Assisted Decision-Making in Sports Training

Lijin Zhu
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引用次数: 8

Abstract

Computer vision has become a fast-developing technology in the field of artificial intelligence, and its application fields are also expanding, thanks to the rapid development of deep learning. It will be of great practical value if it is combined with sports. When a traditional exercise assistance system is introduced into sports training, the athlete’s training information can be obtained by monitoring the exercise process through sensors and other equipment, which can assist the athlete in retrospectively analyzing the technical actions. However, the traditional system must be equipped with multiple sensor devices, and the exercise information provided must be accurate. This paper proposes a motion assistance evaluation system based on deep learning algorithms for human posture recognition. The system is divided into three sections: a standard motion database, auxiliary instruction, and overall evaluation. The standard motion database can be customized by the system user, and the auxiliary teaching system can be integrated. The user’s actions are compared to the standard actions and intuitively displayed to the trainers as data. The system’s overall evaluation component can recognize and display video files, giving trainers an intelligent training platform. Simulator tests are also available. It also demonstrates the efficacy of the algorithm used in this paper.
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运动训练辅助决策的计算机视觉评价系统
计算机视觉已经成为人工智能领域中发展迅速的一项技术,得益于深度学习的快速发展,其应用领域也在不断扩大。如果与体育运动相结合,它将具有很大的实用价值。将传统的运动辅助系统引入到运动训练中,通过传感器等设备对运动过程进行监测,获取运动员的训练信息,帮助运动员对技术动作进行回顾性分析。然而,传统的系统必须配备多个传感器设备,提供的运动信息必须准确。提出了一种基于深度学习算法的人体姿态识别运动辅助评估系统。该系统分为三个部分:标准运动数据库、辅助指令和总体评价。标准的动作数据库可以由系统用户定制,辅助教学系统可以集成。将用户的动作与标准动作进行比较,并直观地作为数据显示给训练人员。该系统的整体评估组件能够识别和显示视频文件,为培训师提供了一个智能化的培训平台。模拟器测试也可用。验证了本文算法的有效性。
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