Enhanced OpenPose Framework for Athlete Motion Analysis via Multi-Classifier Fusion and D-S Decision Rules
Abstract
This study aims to address the issue that traditional analysis methods do not fully consider the differences in the number, types, and equipment occlusion of athletes in actual sports scenes. It constructs an athlete skeleton keypoint detection and action analysis model based on the OpenPose algorithm. The original OpenPose pipeline was optimized by introducing a multi classifier module combining SVM, KNN, and Naive Bayes, as well as a decision model based on conflict judgment and D-S rule fusion. The experiment used a dataset containing 2000 athlete training images (divided into training, testing, and validation sets in a 7:2:1 ratio), and compared it with the original OpenPose and DeepPose baseline models. The results showed that the optimized model had a loss rate of 4.1% on the training set and an accuracy rate of 98%. The error rate of keypoint detection was 7.4%, the speed was 14.2fps, the detection accuracy was 0.92 mAP, and the time consumption was 0.9s. In the case analysis, the highest discrimination rates of the model for football players' running and defensive actions were 0.94 and 0.89, respectively. Moreover, it could further distinguish between subdivided actions such as sprinting and backward running. In addition, the model performed well in different types of athletes (including athletics, weightlifting, gymnastics, etc.) and complex scenes (occlusion, multiple people), with significantly better accuracy and recall than the baseline model (mAP=0.84 for the original OpenPose and mAP=0.78 for DeepPose). The research reveal that the proposed model has higher accuracy and robustness in athlete bone keypoint detection and action analysis, and can provide effective support for scientific training.
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DOI: https://doi.org/10.31449/inf.v49i34.9054
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