Artificial intelligence and ant colony algorithm driven accurate detection method for opera teaching postures

Rapid advances in artificial intelligence and deep learning have enabled accurate analysis of human movements and posture recognition, supporting applications in healthcare, sports training, surveillance, and intelligent education systems. Existing methods lack accuracy in complex opera postures, fail in real-time detection, and do not optimize feature selection effectively. To address these challenges, this study targets complex and standardized opera teaching postures by integrating AI with Ant Colony Optimization (ACO) for accurate detection. It utilizes an Opera Teaching Posture Recognition dataset of 8,701 samples with 18 keypoints, enabling effective multi-class classification and evaluation. Coordinate normalization removes spatial and scale variations, while joint angle features capture geometry, improving robustness and accuracy of posture recognitions. Together, these methods enhance the robustness and accuracy of opera posture recognition. The proposed framework combines ACO- Spatio Temporal-Variational Autoencoder (ACO-ST-VAE) for modeling spatio-temporal relationships in movement sequences and accurate detection for opera teaching postures. The ACO algorithm is employed to optimize feature selection and network parameters, improving convergence speed and reducing computational complexity. ST-VAE models spatio-temporal dependencies in posture sequences, capturing motion dynamics and feature variations to improve accurate and robust posture recognition. Results achieved 98.10% accuracy, 97.80% precision, 97.00% recall, and 97.50% F1-score using the proposed ACO-ST-VAE method implemented in Python. The proposed method effectively identifies subtle posture deviations and provides real-time feedback for teaching and training. It provides a reliable technical solution for intelligent opera education and contributes to the digital preservation and innovative development of traditional opera culture.