Department of Physical Education, North China University of Water Resources and Electric Power, Henan 450046, Zhengzhou, China.
J Healthc Eng. 2022 Mar 22;2022:2174240. doi: 10.1155/2022/2174240. eCollection 2022.
In order to study the action recognition, tracking, and optimization of the training process based on the support vector regression model, a method of human action recognition based on support vector machine optimization is proposed. This method uses the improved strategy of support vector machine to realize the action recognition through the human action recognition based on the optimization of the vector machine. During the recognition, the DAG SVM strategy is improved according to the recognition accuracy of the classifier, and when outputting the result, output the recognition result and the corresponding confidence level, and use the confidence level to process the recognition result. Finally, through the experimental results, it is realized that the recognition rate based on support vector optimization is 98.7%, indicating that this method is effective and can improve the accuracy and efficiency of human body action recognition.
为了研究基于支持向量回归模型的训练过程中的动作识别、跟踪和优化，提出了一种基于支持向量机优化的人体动作识别方法。该方法采用改进的支持向量机策略，通过基于优化的向量机的人体动作识别来实现动作识别。在识别过程中，根据分类器的识别准确率改进 DAG SVM 策略，在输出结果时，输出识别结果和相应的置信度，并使用置信度对识别结果进行处理。最后，通过实验结果，实现了基于支持向量优化的识别率为 98.7%，表明该方法是有效的，可以提高人体动作识别的准确性和效率。
