Abstract

The problem of evaluating a person’s posture from video data is solved. Various key points of the human body are analyzed. We study the change in the accuracy of a fixed model when using different proportions in the regularization term of the loss function. It is shown that for a fixed number of training epochs, the accuracy of the model differs depending on the selected proportions. In addition, it is shown that the linear correlation between the trajectories of the key points that are part of the regularization term is not the main criterion for predicting the effectiveness of applying the regularization term of the loss function.

中文翻译：

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在评估人体姿势问题中使用正则化的特权学习

摘要

解决了从视频数据评估人的姿势的问题。分析人体的各个关键点。我们研究在损失函数的正则化项中使用不同比例时固定模型的准确性的变化。结果表明，对于固定数量的训练周期，模型的准确性根据所选比例的不同而不同。此外，结果表明，作为正则化项一部分的关键点的轨迹之间的线性相关性并不是预测应用损失函数正则化项的有效性的主要标准。