Application of Intelligent Vision Technology in Dance Teaching Action Correction and Standardized Training

Abstract

Under the dual background of digital transformation in art education and deep implementation of artificial intelligence technology, traditional dance teaching relies on subjective judgment by humans, difficulty in unifying movement standards, lagging error correction feedback, and difficulty in large-scale standardized training, which are increasingly prominent pain points. Intelligent vision technology relies on core technologies such as computer vision, deep learning, human pose estimation, and spatiotemporal feature extraction to achieve real-time capture of dance human key points, quantitative analysis of motion poses, construction of standard motion libraries, automatic recognition of deviations, and intelligent error correction feedback, providing a new technological paradigm for dance teaching motion correction and standardized training. This article takes the integration of intelligent visual technology and dance teaching as the research core, and adopts literature research method, system analysis method, technical architecture disassembly method, and application empirical analysis method to conduct systematic research from seven dimensions: theoretical basis, technical system, system construction, error correction mechanism, application scenarios, existing problems and optimization paths, and summary and prospect.