A Computer Vision and AI Pose Estimation-Based Standardized Formation Training and Multi-Scenario Intelligent Judging System

Abstract Manual visual evaluation dominates military and police formation training, athlete scoring and sports event refereeing, which brings inconsistent evaluation standards, strong subjective deviation, low working efficiency and unfair selection results. To solve the above practical defects, this paper designs a universal intelligent evaluation system based on computer vision and human pose estimation. Two core innovative algorithms are proposed in this research: the golden model extraction method based on multi-type benchmark sample data, and the layered adaptive threshold discrimination algorithm constrained by human physiological movement limits. The system builds a dual-logic judging framework including physiological tolerance interval and rigid red-line interval, and realizes modular reuse of underlying algorithms. It can be applied to three major scenarios: standardized formation training for military and police, quantitative scoring of gymnastics, diving and synchronized swimming, and AI auxiliary foul identification and offside judgment for football and basketball matches. Theoretical deduction shows that the system can greatly shorten the standardized training cycle and eliminate artificial scoring bias, realizing full-process digital, fair and automatic evaluation of training and sports competitions. AI Usage Note: All theoretical ideas, mathematical derivations and conclusions are independently created by the author. Generative AI was only used for linguistic polishing of sentences. The author takes full responsibility for all content.