基于长时跟踪的滑雪教学姿态辅助矫正方法OA北大核心CSTPCD

In skiing teaching,the rapid movement and significant changes in the posture of students result in the failure or instability of posture tracking algorithms,which are mainly based on short-term intelligent image change features.The tracking performance can be greatly affected,especially in complex environments such as harsh snow conditions or insufficient lighting.To this end,a skiing-teaching posture correction method based on long-term tracking is proposed.The Recursive Least Squares(RLS)classifier is trained to obtain Kernelized Correlation Filters(KCFs)for student pose and position.Subsequently,the maximum KCF response value is calculated,and the posture position of the student is accurately detected.If the KCF result is below the empirical threshold,it indicates that the target is lost and the re-detection module is activated.Using the optical flow method,the pose position in the current frame becomes close to the position of the student's pose in the previous frame to obtain an approximate position.The tracker is re-applied at this location to obtain accurate student posture positions and achieve long-term tracking.Based on the obtained posture data,a skiing-student posture error compensation model is constructed,and the motion parameters and posture errors of the student's body are extracted.The body motion parameters are calculated and combined with the KCF to construct a skiing-student body posture-assisted correction model,thereby completing posture-assisted correction in skiing teaching.The experimental results show that this method is highly effective,reliable,and stable for long-term tracking.The average PCK index reaches 92.3%,and in terms of target tracking efficiency,the parameters and computational complexity are 30.24 MB and 9.26 GFLOPs,respectively.The speed reaches 142 frame/s,enabling efficient real-time tracking and confirming the feasibility of this method for posture assistance correction in skiing teaching.