A Visual Tracking and Real-Time Error Detection and Correction System for Fingering Trajectories in Music Teaching

The development of accurate fingering technique is fundamental to effective music education, yet traditional instructional approaches rely heavily on manual observation and delayed feedback, limiting real-time correction and objective assessment. This paper presents a visual tracking and real-time error detection and correction system for fingering trajectories in music teaching. The proposed framework integrates computer vision–based hand keypoint detection, spatiotemporal trajectory modeling, and template aligned motion analysis to continuously monitor finger movements during performance. By constructing structured representations of fingering trajectories and aligning them with predefined reference patterns derived from musical scores, the system identifies deviations in timing, finger placement, sequence order, and motion stability. To ensure robustness across different learners, performance speeds, and environmental conditions, an adaptive calibration mechanism is introduced to normalize hand geometry and dynamically adjust detection thresholds. A real-time feedback module provides immediate corrective suggestions, including visual prompts and timing guidance, enabling learners to refine technique during practice sessions. Experimental evaluations conducted across multiple musical excerpts and participants demonstrate the system’s capability to accurately track fingering trajectories and detect common technical errors with high reliability and low latency. The results highlight the potential of the proposed approach to enhance instructional efficiency, promote autonomous learning, and contribute to the advancement of intelligent music education technologies.