Action Style Recognition and Performance Analysis of Cheerleading Based on Perceptron Learning Algorithm
In order to address the inaccuracies in traditional cheerleading style evaluation stemming from an over-reliance on referee'subjective judgments,this paper proposes a method of recognizing and evaluating cheerleading styles based on Perceptron Learning.Cheerleading beginners with uneven positive and negative samples are randomly divided into experimental group and control group.A pattern recognition algorithm based on PU-CNN and recurrent neural network has been employed to extract features from the standard posture input data,and three types of Perceptron Learning style recognition evaluation models have been established to verify the effectiveness of the style recognition algorithm based on PU-CNN and recurrent neural network.In the Perceptron Learning,the scores from the pattern recognition algorithm closely align with those from expert judges.Compared to DT algorithm,P-CNN algorithm and LRCN algorithm,this algorithm achieves the highest recognition accuracy rate of 97.6% and the shortest GPU runtime of 2.2 hours.
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