Movement Evaluation Algorithm-Based Form Tracking Technology and Optimal Control of Limbs for Dancers

This study designs an optimal control model of dance personnel from tracking technology and limb control based on an action evaluation algorithm by constructing a human action evaluation algorithm model and conducting an in-depth study of dance personnel from tracking technology and limb control. This study proposes an OpenPose method based on pose flow optimization to address the false detection of vital human points and misconnection between critical issues in traditional OpenPose-based human pose estimation. The human pose estimation results of OpenPose are optimized by using the human pose flow information in the image sequence. This makes up for the shortcomings of traditional OpenPose that ignores the interframe image information. In this study, we analyze the experimental data of action evaluation, define a set of formulas to evaluate the action after summarizing the distribution pattern of DTW difference sample points from 8 angles, and design an action evaluation system to demonstrate the rationality of this action evaluation method. Since the bases and factors in the evaluation formula are constantly recalculated as the action changes, which increases the complexity of the evaluation method, the following work is to improve the parameters of the activity evaluation formula, so that the evaluation method has better efficiency and adaptability. To enhance the effect of action recognition, this study uses the Kinect sensor to obtain the 3D coordinates of 20 skeletal joints of the human body. It uses the relative distance and angle sequence of the joints as the feature parameters according to the characteristics of human posture. In static pose recognition, the feature vector’s sample set is trained, and the KNN algorithm is used as a classifier to recognize the pose.